Abstract Accurate characterization of aerosol optical depth (AOD) uncertainties is critical for air quality assessment, data assimilation (DA), and environmental studies. In this study, we evaluate two sets of AOD products retrieved from Suomi‐NPP VIIRS over Africa. Specifically, we compare the products of NASA's Dark Target (DT) and Deep Blue (DB) algorithms with co‐located AERONET observations from 2020 to 2024 over Africa. AERONET visible–near‐IR Angstrom Exponents (AE) shows a bimodal distribution and strong monthly variability, with fine‐mode dominance in August–September and coarse‐mode dominance in March–April in this region. When the VIIRS retrievals are collocated with AERONET over Africa, DB shows a slight overestimation with DT showing a slight underestimation. When examined by AOD value range, DB shows a low bias under heavier aerosol loading, whereas DT exhibits a wider data spread and a less pronounced low bias at higher AOD values. Overall, DB demonstrates a higher correlation and a smaller expected error (EE) envelope compared to DT. Analysis of monthly uncertainty indicates that fine‐mode‐dominated months, particularly August, September, and October, which also contain the largest number of moderate to heavy aerosol loading cases, exhibit the lowest uncertainty in the DB retrievals, highlighting the improved performance of the updated algorithm. Our analysis shows that, for both DT and DB, AOD retrieval uncertainties are related to the observed AE, suggesting mismatches between algorithm assumptions and the actual dominant aerosol mode, particularly for coarse and mixed‐mode aerosols.

The use of low-cost sensors (LCS) for air quality monitoring has grown rapidly across a wide range of groups, including community and citizen scientists, academic researchers, environmental agencies, and the private sector. Traditional air monitoring conducted by regulatory agencies relies on expensive, regulatory-grade instruments that require frequent maintenance and rigorous quality control procedures. In contrast, the low purchase price, minimal operating costs, user-friendly design, and open data accessibility have significantly contributed to the widespread adoption of LCS. Over the past decade, hundreds of studies have proposed diverse calibration strategies to tailor LCS performance to specific project needs. This study examines the role of PM2.5 sensors in monitoring air quality across contrasting environments and highlights the importance of inter-sensor consistency. We evaluate PurpleAir (PA) PA-II sensors against regulatory-grade Federal Equivalent Method (FEM) PM2.5 instruments and develop calibration algorithms to improve data accuracy. Calibration deployments were conducted for 2–4 weeks in Raleigh, North Carolina, and Delhi, India, to assess sensor behavior under different aerosol loadings and environmental conditions. The goal of this effort is to create a robust calibration model that uses PA-measured parameters, PM2.5, temperature, and relative humidity as inputs to generate bias-corrected hourly PM2.5 values. The model relies on concurrent FEM PM2.5 measurements as the reference data during calibration development. Multiple statistical and machine-learning approaches were applied to produce a regional calibration model. Our results show that, with proper calibration, PA sensors can provide bias-corrected PM2.5 estimates within 12

Abstract. The launch of NASA's Plankton, Aerosols, Clouds, ocean Ecosystem (PACE) mission in 2024 with the Ocean Color Instrument (OCI) on board created new opportunity for characterizing aerosol properties from space. OCI observes Earth and its atmosphere across a broad spectral range in the reflective spectrum from the ultraviolet (UV) to the Short Wave InfraRed (SWIR). Heritage aerosol retrieval algorithms developed to accommodate radiometers measuring in the visible to SWIR wavelengths such as the MODerate resolution Imaging Spectroradiometer (MODIS) have been successful in characterizing aerosol optical depth (AOD) and indications of aerosol particle size but are less sensitive to aerosol particle absorption or aerosol layer height. Other heritage sensors measuring in the UV part of the spectrum such as the Total Ozone Mapping Spectrometer (TOMS) or the Ozone Monitoring Instrument (OMI) are sensitive to absorption and layer height but have insufficient information to constrain AOD in an aerosol retrieval. With OCI encompassing the entire spectral range of interest, the Dark Target and Deep Blue algorithms from the MODIS tradition and Near UV algorithm from the TOMS/OMI tradition are brought together, adapted for OCI and unified for retrievals of AOD, particle size parameter, aerosol absorption and aerosol layer height. Adaptations for OCI include adjustment for OCI sensor characteristics, modification of cloud and snow masking routines, production of the traditional UV Aerosol Index at unprecedented 1 km resolution, merging of the Dark Target and Deep Blue retrievals over land, extrapolation of spectral AOD retrieved in the visible range into the UV range, use of Oxygen-B bands for aerosol layer height, and retrieval of AOD above clouds that provides visualization of All Sky aerosol loading, Six months of retrievals have been compared with collocated AERONET AOD and single scattering albedo (SSA) resulting in a preliminary validation. AOD is biased high for shorter wavelengths, especially over ocean leading to asymmetrical error bounds for AOD. No similar bias is seen in the SSA retrievals which are exhibiting error bounds of ±0.04, at this stage. The results of the OCI Unified Aerosol Algorithm are quantitative characterization of the global aerosol system over ocean, vegetated and barren surfaces, in clear skies and above clouds.

The summer of 2023 was the most significant wildfire and smoke season on record in Canada. Data from five different satellite instruments going back to 2001 show that Canada and most provinces and territories experienced peak visible‐wavelength aerosol optical depth and ultraviolet aerosol index values in 2023. Longer‐term, 2023 had the highest number of “smoke” or “haze” reports in weather records by a factor of two compared with the previous record in 1981, and by a factor of seven compared with the 1953–2022 average. These reports show an east‐to‐west shift in Canada's summer air pollution patterns. Smoke and haze in eastern Canada have decreased since the 1980s because of pollution control measures domestically and in the US. On the other hand, wildfire smoke has increased in the Northwest Territories, British Columbia, Alberta, and Saskatchewan since the 2010s, and is now the main air quality concern in western Canada. Interpreting the analysis here for Canada alongside previous work over the US, there was a shift over North America in summer air quality concerns from the east to the west. Climate model projections suggest more wildfire‐driven smoke in the future throughout North America, particularly in the west. In contrast to air pollution from smokestacks and tailpipes that can be addressed at the source through government regulation, a future with more wildfire smoke will require downwind mitigation and will be the responsibility of public health officials.

In recent years, increasing wildfire activity in the western United States has led to significant emissions of smoke aerosols, impacting the atmospheric energy balance through their absorption and scattering properties. Single scattering albedo (SSA) is a key parameter that governs these radiative effects, but accurately retrieving SSA from satellites remains challenging due to limitations in sensor resolution, low sensitivity of traditional remote sensing methods, and uncertainties in radiative transfer modeling, particularly from surface reflectance and aerosol characterization. Smoke optical properties evolve rapidly after emission, influenced by fuel type, combustion conditions, and chemical aging. Accurate SSA retrieval near the source thus requires high-temporal-resolution satellite observations. Critical Reflectance (CR) method provides this capability by identifying a unique reflectance value at which top-of-atmosphere (TOA) reflectance becomes insensitive to aerosol loading and primarily reflects aerosol absorption. SSA can be retrieved from this critical reflectance. This study presents a geostationary-based CR method using the Advanced Baseline Imager (ABI) on GOES-R satellites. The approach leverages ABI's high temporal (5-10 min) and spatial (3 km) resolution, consistent viewing geometry, and wide coverage. A tailored look-up table, based on an AOD-dependent smoke model for North America, links CR to SSA. Case studies show strong agreement with AERONET measurements, with retrieval differences mostly within 0.01-well below AERONET's +/- 0.03 uncertainty. The method captures temporal and spatial variations in smoke absorption and demonstrates robustness across daylight hours. This GEO-based CR approach offers an effective tool for high-resolution SSA retrieval, contributing to improved aerosol radiative forcing estimates and climate modeling.

The improvement of satellite-derived calculations of direct aerosol radiative effects (DARE) is essential for reducing the uncertainty in the impact of aerosol on solar radiation. We develop a framework to compute DARE at the top of the Earth's atmosphere, in the shortwave part of the electromagnetic spectrum, and in all-sky conditions along the track of the A-Train constellation of satellites. We use combined state-of-the-art aerosol and cloud properties from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors. We also use a global reanalysis from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) to provide vertical distribution of aerosol properties and atmospheric conditions. Diurnal mean satellite DARE values range from −25 W m−2 (cooling) to 40 W m−2 (warming) over the southeast Atlantic during 3 days from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) aircraft campaign. These 3 days indicate agreement between our satellite-calculated DARE and co-located airborne Solar Spectral Flux Radiometer (SSFR) measurements. This paper constitutes the first step before applying our algorithm to more years of combined satellite and model data over more regions of the world. The goal is to ultimately assess the order of importance of atmospheric parameters in the calculation of DARE for specific aerosol and cloud regimes. This will inform future missions about where, when, and how accurately the retrievals should be performed to reduce all-sky DARE uncertainties.

The Aralkum Desert presents a challenging environment for satellite aerosol observations due to its very bright, heterogeneous, and dynamic surfaces and the lack of in situ constraints on region-specific aerosol properties. We survey current global satellite algorithms capable of detecting the presence, column burden, and elevation of airborne dust over the Aral Sea basin. Discrepancies and potential biases in retrieved UV aerosol index (UVAI), mid-visible and thermal infrared optical depth (AOD), and layer height due to different assumptions on surface and aerosol properties are assessed. The results indicate that (1) UVAI products consistently delineate dust plume extent but show large positive values over turbid waters and salt flats due to enhanced surface absorption. (2) MODIS and VIIRS total and coarse-mode AOD retrievals show strong agreement over the Caspian Sea despite using different aerosol optical models. Over desert surfaces, all operational AOD products misclassify fresh dust plumes as clouds and exhibit strong nonlinear relationships. The NOAA EPS algorithm retrieves significantly lower AOD than others, although the agreement improves when a dust optical model is used. The MISR research algorithm produces higher, more consistent AOD and improved particle property retrievals compared to the MISR operational product. (3) Among four IASI infrared products, the LMD algorithm performs best in detecting dust plume features over both desert and water surfaces. (4) The EPIC aerosol optical centroid height (AOCH) product overestimates dust layer altitude under low aerosol loadings but exhibits good agreement with CALIOP in detecting the elevated dust characterized by well-defined upper boundaries. MISR height retrievals also align well with CALIOP and EPIC. IASI infrared retrievals are about 0.4 km higher than EPIC over dust-laden scenes. This study underscores the value of a synergistic, multisensor approach leveraging the complementary strengths of satellite aerosol products and calls for their appropriate application and careful interpretation when characterizing saline dust from the Aralkum Desert.

Aerosol optical depth (AOD) is a crucial data record to understand aerosols and their direct and indirect effects on air quality and climate forcing. The Dark Target aerosol retrieval product includes AOD and other properties derived from multispectral satellite imagers, available for MODIS on Terra (from 2000), MODIS on Aqua (from 2002), and VIIRS on Suomi-NPP (from 2012). Although Terra now has over 25 years of observations, the record must continue onto VIIRS beyond the end of the MODIS mission to meet requirements as a Global Climate Observing System (GCOS) climate data record. We present the recent update to version 2.0 of the VIIRS product, which now includes NOAA-20 VIIRS (from 2017) and algorithm improvements. The combined MODIS-VIIRS dataset is examined for consistency and to ascertain aerosol trends. Overall, the VIIRS products show consistency with the MODIS products. To assess regional trends, two time intervals are studied: a 22-year record that compares Terra and Aqua, and a more recent 12-year record (the VIIRS era) that compares three sensors. According to linear regressions of monthly average AOD for each global 1°×1° grid cell, AOD has decreased by between 0.003 and 0.01 per year over parts of China, the United States, Brazil, and much of Europe, while increasing on the same scale over India and parts of Canada, while more modestly but significantly increasing over the southern oceans. For seven regions with significant AOD trends, this study examines the seasonal dependence, relationship to aerosol size parameters, and whether the sign or magnitude of these trends have changed. With high agreement among sensors, we are confident that the Dark Target AOD record can extend into the 2030s and beyond.

Background: Low back pain (LBP) is a highly prevalent, disabling condition affecting millions of people. Patients with an identifiable anatomic pain generator and resulting neuropathic lower extremity symptoms often undergo spine surgery, but many patients lack identifiable and/or surgically corrective pathology. Nonoperative treatment options often fail to provide sustained relief. Spinal cord stimulation (SCS) is sometimes used to treat these patients, but the lack of level 1 evidence limits its widespread use and insurance coverage. The DISTINCT RCT study evaluates the efficacy of passive recharge burst SCS compared to conventional medical treatment (CMM) in alleviating chronic, refractory axial low back pain. Methods: This prospective, multicenter, randomized, study with an optional 6-month crossover involved patients who were not candidates for lumbar spine surgery. The primary and secondary endpoints evaluated improvements in low back pain intensity (NRS), back pain-related disability (ODI), pain catastrophizing (PCS), and healthcare utilization. Patients were randomized to SCS therapy or CMM at 30 US study sites. Results: The SCS arm reported an 85.3% NRS responder rate (>= 50% reduction) compared to 6.2% (5/81) in the CMM arm. After the 6M primary endpoint, SCS patients elected to remain on assigned therapy and 66.2% (49/74) of CMM patients chose to trial SCS (crossover). At the 12M follow-up, SCS and crossover patients reported 78.6% and 71.4% NRS responder rates. Secondary outcomes indicated significant improvements in ODI, PCS, and reduced healthcare utilization. Six serious adverse events were reported and resolved without sequelae. Conclusion: DISTINCT chronic low back pain patients with no indication for corrective surgery experienced a significant and sustained response to burst SCS therapy for up to 12 months. CMM patients who crossed over to the SCS arm reported profound improvements after 6 months. This data advocates for a timely consideration of SCS therapy in patients unresponsive to conservative therapy.

IntroductionThe International Neuromodulation Society convened a multispecialty group of physicians and scientists based on expertise with international representation to establish evidence-based guidance on intrathecal drug delivery in treating chronic pain. This Polyanalgesic Consensus Conference (PACC)® project, created more than two decades ago, intends to provide evidence-based guidance for important safety and efficacy issues surrounding intrathecal drug delivery and its impact on the practice of neuromodulation.Materials and MethodsAuthors were chosen on the basis of their clinical expertise, familiarity with the peer-reviewed literature, research productivity, and contributions to the neuromodulation literature. Section leaders supervised literature searches of MEDLINE, BioMed Central, Current Contents Connect, Embase, International Pharmaceutical Abstracts, Web of Science, Google Scholar, and PubMed from 2017 (when PACC® last published guidelines) to the present. Identified studies were graded using the United States Preventive Services Task Force criteria for evidence and certainty of net benefit. Recommendations are based on the strength of evidence or consensus when evidence is scant.ResultsThe PACC® examined the published literature and established evidence- and consensus-based recommendations to guide best practices. Additional guidance will occur as new evidence is developed in future iterations of this process.ConclusionsThe PACC® recommends best practices regarding intrathecal drug delivery to improve safety and efficacy. The evidence- and consensus-based recommendations should be used as a guide to assist decision-making when clinically appropriate.

Retrieval of aerosol properties near clouds from passive remote sensing is challenging. Sunlight scattered by clouds into nearby clear regions can effectively enhance the clear area reflectance. These cloud 3D radiative effects may lead to large biases in aerosol retrievals if uncorrected, risking the incorrect interpretation of satellite observations for aerosol–cloud interaction in a cloudy atmosphere. In earlier studies, we developed a simple two-layer model (2LM) to estimate the cloud-induced clear-sky radiance enhancements in cloud fields. In this study, we take advantage of CALIPSO lidar observations, which should not be affected by the 3D radiative effect, to study passive aerosol retrievals in cloud fields in the Amazon region, specifically those produced by the operational Dark Target algorithm applied to Aqua-MODIS. From 2 years’ worth of co-located CALIPSO/MODIS aerosol retrievals, we find a larger increase in operationally retrieved MODIS AOD from clear to cloudy regions (∼0.075 or ∼40%) than for the CALIPSO AOD (∼0.021 or ∼20%). The much larger increase in MODIS AOD is mainly due to the 3D radiative effects. After using the 2LM model to account for cloud 3D radiative effects, the clear to cloudy increase in MODIS AOD was reduced to ∼0.043 (∼23%), which is much closer to CALIPSO observations. The 3D corrected average MODIS AOD for cloudy conditions is significantly larger than AOD for clear conditions, even for cloud fraction (CF) less than 0.1, suggesting aerosols in cloudy conditions are characteristically different from aerosols in clear conditions. Furthermore, the 3D correction of AOD (i.e., τ1D−τ3D) increases linearly with CF for a large range of CF. We have also examined the impact of the 3D effect on aerosol Ångström Exponent (AE) and fine model fraction (FMF) of AOD. We found that the uncorrected average AE and FMF depend strongly on CF, ∼25% increase in AE (decrease in particle size) and ∼60% increase in FMF as CF increases from 0.05 to 0.45. The 3D correction leads to smaller average AE (bigger particle size) and FMF that are almost independent of CF. Thus, the 3D corrected aerosol properties are expected to provide more accurate information for better understanding aerosol–cloud interactions.

Introduction:Chronic pain is a personal experience influenced by multiple biopsychosocial factors. Using a pain intensity measure alone to assess the effectiveness of a chronic pain intervention fails to fully evaluate its impact on the multifaceted chronic pain experience. The holistic minimal clinically important difference (MCID) is a composite outcome developed to provide a comprehensive assessment of chronic pain in response to intervention, across 5 outcome domains: pain intensity, health-related quality of life, sleep quality, physical, and emotional function. To focus on domains where the individual need is greatest, the holistic MCID reflects the cumulative MCID averaged over only the domains where subjects were impaired preintervention. Objectives:To assess the internal and construct validity of the Holistic MCID score to inform its future use as an evidence-based tool. Methods:This validation study was undertaken using data from the EVOKE trial with 111 patients up to 24-month follow-up. Internal consistency of the holistic MCID was assessed using Cronbach alpha statistic and dimensional exploration using principal component analysis. Results:The holistic MCID measure demonstrated strong internal consistency with Cronbach alpha >0.7 at all follow-ups. Principal component analysis showed one overarching holistic dimension to be present in the composite. Construct validity was demonstrated by an increase in the holistic MCID score being associated with both increased Patients' Global Impression of Change, EuroQol visual analogue scale score, and each of the outcome domains in a "leave-one-out" analysis (all P < 0.001). Conclusion:The holistic MCID provides a valid measure for the comprehensive, personalized assessment of response after a chronic pain intervention. The validity of the holistic MCID requires further confirmation in other chronic pain populations and with different interventions.

This comprehensive study analyzed aerosol products from six low-Earth orbit (LEO) and geostationary Earth orbit (GEO) sensors. LEO sensors like the MODerate resolution Imaging Spectroradiometer (MODIS) and VIsible InfraRed Suite (VIIRS) provide one to two daily global measurements, while GEO sensors (Advanced Himawari Imager: AHI, Advanced Baseline Imager: ABI) offer high-frequency data (similar to 10 min) over specific regions. The combination of LEO and GEO capabilities offers expanded coverage of the global aerosol system if aerosol retrievals are applied consistently across all sensors and packaged in an easy-to-use product. The Dark Target aerosol retrieval algorithm was applied to the six sensors, and the resulting Level 2 aerosol optical depth (AOD) products were gridded and merged into a Level 3 quarter-degree latitude-longitude grid with a 30 min temporal resolution, providing the necessary consistency and packaging. Validation of this packaged Level 3 AOD product against Aerosol Robotics NETwork (AERONET) measurements across global locations showcased the merged product's robustness with a correlation coefficient of 0.83, revealing a global mean bias of approximately +/- 0.05, with 65.5 % of retrievals falling within an expected uncertainty range, underlining the reliability of the dataset. The new gridded Level 3 dataset significantly improved daily global coverage to nearly 45 %, overcoming the limitations of individual sensors, which typically range from 12 % to 25 %. Furthermore, this merged dataset approximates the diurnal cycle of AOD observed by AERONET, thus offering insights into diurnal signatures retrieved elsewhere. The resulting dataset's high spatiotemporal resolution and improved global coverage, especially in regions covered by GEO sensors (Americas and Asia), make it a valuable tool for diverse applications. Tracking aerosol transport from phenomena like wildfires and dust storms is gaining precision, enabling enhanced air quality forecasting and hindcasting. Additionally, the study positions the merged dataset as a significant asset for evaluating and intercomparing regional or global model simulations, which was previously unattainable in such a gridded format. The dataset and fusion framework layout in this study have the potential to include data from recently (future) launched other GEO (FCI, AMI) and LEO (PACE, VIIRS-JPSS) sensors.

Starting from point sources, wildfire smoke is important in the global aerosol system. The ability to characterize smoke near-source is key to modeling smoke dispersion and predicting air quality. With hemispheric views and 10-min refresh, imagers in Geostationary (GEO) orbit have advantages monitoring smoke over once-per-day sensors in low-earth orbit (LEO). However, both can be inadequate in capturing the characteristics of smoke plumes close to their sources due to too-coarse spatial resolution (both detector and product resolution), too-sparse temporal resolution (from LEO sensors), and too-conservative masking. In addition to satellite observations, the Fire Influence on Regional to Global Environments and Air Quality experiment offered sub-orbital enhanced-MODIS Airborne Simulator (eMAS) imagery at 50 m pixel resolution-including multiple eMAS flight tracks over individual fires in short time periods. It provided opportunity to explore smoke plume characterization at various spatial and temporal scales and quantify the limitations of space sensors for describing smoke magnitude near source as well as its temporal evolution. Here we applied modified aerosol algorithm to different imagers, relaxing its masking to estimate smoke's aerosol optical depth (AOD) as close as possible to its source. We found that GEO sensors with nominal 1 km spatial resolution can match the much finer resolution eMAS retrieved mean plume AOD, as long as the retrieval spatial resolution is finer than the width of the plumes. However, the plume's maximum AOD may be drastically underestimated by satellite products. Starting from point sources, wildfire smoke is important in the global aerosol system. The ability to characterize smoke near-source is key to modeling smoke dispersion and predicting air quality. Satellite-based sensors provide important information about smoke plumes, however, by themselves can be inadequate in capturing the characteristics of smoke plumes close to their sources. In addition to geostationary orbit (GEO) satellite observations with 1-2 km spatial resolution observations at 10-min intervals and low-earth orbit (LEO) at 0.5-1 km resolution observations daily, the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) experiment offered sub-orbital (airborne) enhanced-MODIS Airborne Simulator (eMAS) imagery at 50 m pixel resolution. With multiple eMAS flight tracks over individual fires in short time periods, FIREX-AQ provided opportunity to explore smoke plume characterization at various spatial and temporal scales and quantify the limitations of space sensors for describing smoke magnitude near source as well as its temporal evolution. Here we applied a modified aerosol algorithm to all the different imagers, relaxing its masking to estimate smoke's aerosol optical depth (AOD) as close as possible to its source. We found that GEO sensors with nominal 1 km spatial resolution can match the much finer resolution eMAS retrieved mean plume AOD, as long as the retrieval spatial resolution is finer than the width of the plumes. However, the plume's maximum AOD may be drastically underestimated by satellite products. Remote-sensed imagery of smoke plumes at different spatial and temporal resolutions are used to observe smoke magnitude and dispersion near fire sourcesHigh-resolution, sub-orbital imagery helps quantify limitations of lower-resolution orbiting sensors for observing smoke's magnitude and evolutionSatellite products with spatial resolution less than the width of the plume can capture the mean but not the maximum smoke loading

Introduction A novel, spinal cord stimulation (SCS) system with a physiologic closed-loop (CL) feedback mechanism controlled by evoked compound action potentials (ECAPs) enables the optimization of physiologic neural dose and the accuracy of the stimulation, not possible with any other commercially available SCS systems. The report of objective spinal cord measurements is essential to increase the transparency and reproducibility of SCS therapy. Here, we report a cohort of the EVOKE double-blind randomized controlled trial treated with CL-SCS for 36 months to evaluate the ECAP dose and accuracy that sustained the durability of clinical improvements. Methods 41 patients randomized to CL-SCS remained in their treatment allocation and were followed up through 36 months. Objective neurophysiological data, including measures of spinal cord activation, were analyzed. Pain relief was assessed by determining the proportion of patients with >= 50% and >= 80% reduction in overall back and leg pain. Results The performance of the feedback loop resulted in high-dose accuracy by keeping the elicited ECAP within 4 mu V of the target ECAP set on the system across all timepoints. Percent time stimulating above the ECAP threshold was >98%, and the ECAP dose was >= 19.3 mu V. Most patients obtained >= 50% reduction (83%) and >= 80% reduction (59%) in overall back and leg pain with a sustained response observed in the rates between 3-month and 36-month follow-up (p=0.083 and p=0.405, respectively). Conclusion The results suggest that a physiological adherence to supra-ECAP threshold therapy that generates pain inhibition provided by ECAP-controlled CL-SCS leads to durable improvements in pain intensity with no evidence of loss of therapeutic effect through 36-month follow-up.

Background:The Consolidated Standards of Reporting Trials (CONSORT) statement has improved the quality of reporting of randomised trials. Extensions to the CONSORT statement are often needed to address specific issues of trial reporting, including those relevant to particular types of interventions. Methodological and reporting deficiencies in clinical trials of implantable neurostimulation devices are common. The CONSORT-iNeurostim extension is a new reporting guideline for randomised controlled trials evaluating implantable neurostimulation devices. Methods:CONSORT-iNeurostim was developed using the EQUATOR methodological framework including a literature review and expert consultation to generate an initial list of candidate items. The candidate items were included in a two-round Delphi survey, discussed at an international consensus meeting (42 stakeholders including healthcare professionals, methodologists, journal editors and industry representatives from the United States, United Kingdom, Netherlands and other countries), and refined through a checklist pilot (18 stakeholders). Findings:The initial extension item list included 49 candidate items relevant to CONSORT-iNeurostim. We received 132 responses in the first round of the Delphi survey and 99 responses in the second round. Participants suggested an additional 20 candidate items for CONSORT-iNeurostim during the first round of the survey, and those achieving initial consensus were discussed at the consensus meeting. The CONSORT-iNeurostim extension includes 7 new checklist items, including one item for reporting the neurostimulation intervention comprising a separate checklist of 14 items. Interpretation:The CONSORT-iNeurostim extension will promote increased transparency, clarity, and completeness of trial reports of implantable neurostimulation devices. It will assist journal editors, peer-reviewers, and readers to better interpret the appropriateness and generalisability of the methods used and reported outcomes. Funding:Abbott, Boston Scientific Corp., Mainstay Medical, Medtronic Ltd, Nevro Corp. and Saluda Medical.