BACKGROUND:The COVID-19 pandemic has yielded an unprecedented quantity of new publications, contributing to an overwhelming quantity of information and leading to the rapid dissemination of less stringently validated information. Yet, a formal analysis of how the medical literature has changed during the pandemic is lacking. In this analysis, we aimed to quantify how scientific publications changed at the outset of the COVID-19 pandemic.METHODS:We performed a cross-sectional bibliometric study of published studies in four high-impact medical journals to identify differences in the characteristics of COVID-19 related publications compared to non-pandemic studies. Original investigations related to SARS-CoV-2 and COVID-19 published in March and April 2020 were identified and compared to non-COVID-19 research publications over the same two-month period in 2019 and 2020. Extracted data included publication characteristics, study characteristics, author characteristics, and impact metrics. Our primary measure was principal component analysis (PCA) of publication characteristics and impact metrics across groups.RESULTS:We identified 402 publications that met inclusion criteria: 76 were related to COVID-19; 154 and 172 were non-COVID publications over the same period in 2020 and 2019, respectively. PCA utilizing the collected bibliometric data revealed segregation of the COVID-19 literature subset from both groups of non-COVID literature (2019 and 2020). COVID-19 publications were more likely to describe prospective observational (31.6%) or case series (41.8%) studies without industry funding as compared with non-COVID articles, which were represented primarily by randomized controlled trials (32.5% and 36.6% in the non-COVID literature from 2020 and 2019, respectively).CONCLUSIONS:In this cross-sectional study of publications in four general medical journals, COVID-related articles were significantly different from non-COVID articles based on article characteristics and impact metrics. COVID-related studies were generally shorter articles reporting observational studies with less literature cited and fewer study sites, suggestive of more limited scientific support. They nevertheless had much higher dissemination.

BACKGROUND Emergence delirium is a common complication in paediatric anaesthesia associated with significant morbidity. Total intravenous anaesthesia (TIVA) and intra-operative dexmedetomidine as an adjuvant to sevoflurane anaesthesia can both reduce the incidence of emergence delirium compared with sevoflurane alone, but no studies have directly compared their relative efficacy. OBJECTIVE The study objective was to compare the effects of TIVA and dexmedetomidine on the incidence of paediatric emergence delirium. STUDY DESIGN The current study is a systematic review and network meta-analysis (NMA) of randomised controlled trials. DATA SOURCES We conducted a systematic search of 12 databases including Medline (Ovid) and Web of Science (Clarivate Analytics) from their respective inception to December 2020. ELIGIBILITY Inclusion criteria were randomised controlled trials of paediatric patients undergoing general anaesthesia using sevoflurane, sevoflurane with dexmedetomidine or TIVA. Data were extracted by two reviewers according to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines and analysed using NMA methodology. Risk ratios and 95% credible intervals (CrI) were calculated for all outcomes [emergence delirium, postoperative nausea and vomiting (PONV), and time to emergence and extubation]. The protocol was registered with PROSPERO (CRD42018091237). RESULTS The systematic review returned 66 eligible studies comprising 5257 patients with crude median emergence delirium incidences of 12.8, 9.1 and 40% in the dexmedetomidine with sevoflurane, TIVA and sevoflurane alone groups, respectively. NMA indicated that compared with TIVA, sevoflurane with adjuvant dexmedetomidine decreased the incidence of emergence delirium without statistical difference (risk ratio 0.88, 95% CrI 0.61 to 1.20, low quality of evidence), but resulted in a higher incidence of PONV (risk ratio: 2.3, 95% CrI 1.1 to 5.6, low quality of evidence). CONCLUSION Clinical judgement, considering the patient's risk factors for the development of clinically significant outcomes such as emergence delirium and PONV, should be used when choosing between TIVA and sevoflurane with adjuvant dexmedetomidine. These findings are limited by the low quality of evidence (conditional recommendation).

Pediatric AnesthesiaVolume 31, Issue 2 p. 234-236 SHORT REPORT Anesthetic management and outcomes for MRI-guided laser interstitial thermal therapy (LITT) for seizure focus in pediatrics: A single-centre experience with 10 consecutive patients David Neville Levin, Corresponding Author david.levin@sickkids.ca orcid.org/0000-0001-5404-9828 Department of Anesthesia, University of Toronto Faculty of Medicine, Toronto, ON, Canada Anesthesia and Pain Medicine, Hospital for Sick Children, Toronto, ON, Canada Correspondence David Neville Levin, Department of Anesthesia, University of Toronto Faculty of Medicine, 555 University Avenue Black Wing, 2nd Floor, Room 2418, Toronto, ON M5G 1X8, Canada. Email: david.levin@sickkids.caSearch for more papers by this authorCraig D. McClain, Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, USASearch for more papers by this authorScellig S. D. Stone, Neurosurgery, Boston Children's Hospital, Boston, MA, USASearch for more papers by this authorJoseph R. Madsen, Neurosurgery, Boston Children's Hospital, Boston, MA, USASearch for more papers by this authorSulpicio Soriano, Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, USASearch for more papers by this author David Neville Levin, Corresponding Author david.levin@sickkids.ca orcid.org/0000-0001-5404-9828 Department of Anesthesia, University of Toronto Faculty of Medicine, Toronto, ON, Canada Anesthesia and Pain Medicine, Hospital for Sick Children, Toronto, ON, Canada Correspondence David Neville Levin, Department of Anesthesia, University of Toronto Faculty of Medicine, 555 University Avenue Black Wing, 2nd Floor, Room 2418, Toronto, ON M5G 1X8, Canada. Email: david.levin@sickkids.caSearch for more papers by this authorCraig D. McClain, Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, USASearch for more papers by this authorScellig S. D. Stone, Neurosurgery, Boston Children's Hospital, Boston, MA, USASearch for more papers by this authorJoseph R. Madsen, Neurosurgery, Boston Children's Hospital, Boston, MA, USASearch for more papers by this authorSulpicio Soriano, Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, USASearch for more papers by this author First published: 23 May 2020 https://doi.org/10.1111/pan.13929Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume31, Issue2February 2021Pages 234-236 RelatedInformation

In most automatic speech recognition (ASR) systems, the audio signal is processed to produce a time series of sensor measurements (e.g., filterbank outputs). This time series encodes semantic information in a speaker-dependent way. An earlier paper showed how to use the sequence of sensor measurements to derive an "inner" time series that is unaffected by any previous invertible transformation of the sensor measurements. The current paper considers two or more speakers, who mimic one another in the following sense: when they say the same words, they produce sensor states that are invertibly mapped onto one another. It follows that the inner time series of their utterances must be the same when they say the same words. In other words, the inner time series encodes their speech in a manner that is speaker-independent. Consequently, the ASR training process can be simplified by collecting and labelling the inner time series of the utterances of just one speaker, instead of training on the sensor time series of the utterances of a large variety of speakers. A similar argument suggests that the inner time series of music is instrument-independent. This is demonstrated in experiments on monophonic electronic music.

This case demonstrates the airway management of a pediatric patient with short stature due to STAT5b deficiency, a rare genetic immunodeficiency associated with lung disease and endocrinopathy. The patient had recurrent pulmonary infections and pulmonary alveolar proteinosis (PAP) for which whole lung lavage (WLL) was recommended. Due to short stature and overall body habitus, the patient’s airway would not accommodate a traditional double-lumen tube (DLT). Therefore, we placed 2 single-lumen breathing tubes: 1 endobronchial and 1 endotracheal, to mimic a DLT and facilitate WLL, demonstrating a viable option for lung isolation in the absence of purpose-built equipment.

Purpose Nabilone is a synthetic cannabinoid with properties that make it an appealing candidate as a postoperative nausea and vomiting (PONV) prophylactic adjunct. Nabilone has proven clinical utility in chemotherapy-related nausea and vomiting but has not been adequately tested for PONV. The purpose of this study was to evaluate the effectiveness of a single dose of nabilone for the prevention of PONV.Methods This was a pragmatic single-centre randomized-controlled trial comparing oral nabilone vs placebo for the prevention of PONV. Eligible patients scheduled for elective surgery under general anesthesia who had a preoperative risk of PONV greater than 60% received either nabilone 0.5 mg or placebo orally prior to surgery. As part of the pragmatic design, the study medication was given in addition to any other combination of antiemetic prophylaxis. The primary outcome was the incidence of PONV. Secondary outcomes included the effect on pain, speed of recovery, and drug side effects.Results Of the 340 patients randomized, 172 received nabilone and 168 received placebo. There was no difference in the incidence of PONV, which occurred in 20.9% in the nabilone group and 21.4% in the placebo group (relative risk, 0.98; 95% confidence interval, 0.89 to 1.11; P = 0.99). There were also no differences in pain scores, opioid consumption, or reported drug side effects.Conclusion Oral nabilone 0.5 mg given as a single dose prior to surgery is ineffective in reducing PONV. This trial was registered at ClinicalTrials.gov, identifier: NCT02115529.

Consider a time series of measurements of the state of an evolving system, x(t), where x has two or more components. This paper shows how to perform nonlinear blind source separation; i.e., how to determine if these signals are equal to linear or nonlinear mixtures of the state variables of two or more statistically independent subsystems. First, the local distributions of measurement velocities are processed in order to derive vectors at each point in x-space. If the data are separable, each of these vectors must be directed along a subspace of x-space that is traversed by varying the state variable of one subsystem, while all other subsystems are kept constant. Because of this property, these vectors can be used to construct a small set of mappings, which must contain the unmixing function, if it exists. Therefore, nonlinear blind source separation can be performed by examining the separability of the data after it has been transformed by each of these mappings. The method is analytic, constructive, and model-independent. It is illustrated by blindly recovering the separate utterances of two speakers from nonlinear combinations of their audio waveforms.

This paper shows how a time series of measurements of an evolving system can be processed to create an inner time series that is unaffected by any instantaneous invertible, possibly nonlinear transformation of the measurements. An inner time series contains information that does not depend on the nature of the sensors, which the observer chose to monitor the system. Instead, it encodes information that is intrinsic to the evolution of the observed system. Because of its sensor-independence, an inner time series may produce fewer false negatives when it is used to detect events in the presence of sensor drift. Furthermore, if the observed physical system is comprised of non-interacting subsystems, its inner time series is separable; i.e., it consists of a collection of time series, each one being the inner time series of an isolated subsystem. Because of this property, an inner time series can be used to detect a specific behavior of one of the independent subsystems without using blind source separation to disentangle that subsystem from the others. The method is illustrated by applying it to: 1) an analytic example; 2) the audio waveform of one speaker; 3) video images from a moving camera; 4) mixtures of audio waveforms of two speakers.

Consider a time series of signal measurements x(t), where x has two components. This paper shows how to process the local distributions of measurement velocities in order to construct a two-component mapping, u(x). If the measurements are linear or nonlinear combinations of statistically independent variables, u(x) must be an unmixing function. In other words, the measurement data are separable if and only if $$u_{1}[x(t)]$$ and $$u_{2}[x(t)]$$ are statistically independent of one another. The method is analytic, constructive, and model-independent. It is illustrated by blindly recovering the separate utterances of two speakers from nonlinear combinations of their waveforms.

Background:‘New’ deviancy theories came to prominence during the 1960s and presented a significant challenge to established ways of thinking about crime, delinquency and other forms of rule-breaking. These theories dismissed the idea that there is a distinct, unambiguously deviant minority whose behavior can be explained as a result of individual pathology or social dysfunction. Instead, it was argued that deviance involves meaningful and goal-oriented behavior, which can only be understood through an appreciative stance that is committed to faithful understanding of the world as seen by the subject. Methods and Aims:This paper focuses on the application of ‘new’ deviancy theories to the progression from medically appropriate prescription drug use to extra-medical ‘abuse’. Special consideration is given to the role of the prescribing physician and the medical institution. Conclusions:‘New’ deviancy theories lend valuable insights into contemporary patterns of unauthorized prescription drug use. They bring to light the role of the physician-patient interaction as a mechanism to diagnose ‘misuse’ by searching for use of neutralization techniques, and for its function in facilitating future ‘abuse’ by guiding a patient through the learned steps to become a regular user. They highlight the importance of values in a patient’s choice to accept medications with psychoactive side effects, and they reinforce the subjectivity in diagnosis and labeling misuse. These theories illustrate the complexities of the interplay between social welfare support, disability, societal norms and self-identity, which are all critical parts of the patient experience. Finally, these concepts help generate hypothesis about the development of meaningful subcultural groups based around this type of behavior. An appreciation of drug ‘abuse’ through this historical framework can inform new approaches for drug policy aimed at reducing narcotic drug abuse.

Consider a time series of signal measurements x(t), having components x_k k = 1,2, … ,N. This paper shows how to determine if these signals are equal to linear or nonlinear mixtures of the state variables of two or more statistically-independent subsystems. First, the local distribution of measurement velocities (ẋ) is processed in order to derive N local vectors at each x. If the data are separable, each of these vectors is directed along a subspace traversed by varying the state variable of one subsystem, while all other subsystems are kept constant. Because of this property, these vectors can be used to determine if the data are separable, and, if they are, x(t) can be transformed into a separable coordinate system in order to recover the time series of the independent subsystems. The method is illustrated by using it to blindly recover the separate utterances of two speakers from nonlinear combinations of their waveforms.

Given a time series of multicomponent measurements x ( t ), the usual objective of nonlinear blind source separation (BSS) is to find a ¿source¿ time series s ( t ), comprised of statistically independent combinations of the measured components. In this paper, the source time series is required to have a density function in (s , \mathdot s )-space that is equal to the product of density functions of individual components. This formulation of the BSS problem has a solution that is unique, up to permutations and component-wise transformations. Separability is shown to impose constraints on certain locally invariant (scalar) functions of x , which are derived from local higher-order correlations of the data's velocity \mathdot x . The data are separable if and only if they satisfy these constraints, and, if the constraints are satisfied, the sources can be explicitly constructed from the data. The method is illustrated by using it to recover the contents of two simultaneous speech-like sounds recorded with a single microphone.

Given a time series of multicomponent measurements x (t ), the usual objective of nonlinear blind source separation (BSS) is to find a "source" time series s (t ), comprised of statistically independent combinations of the measured components. In this paper, the source time series is required to have a density function in $(s,\dot{s})$-space that is equal to the product of density functions of individual components. This formulation of the BSS problem has a solution that is unique, up to permutations and component-wise transformations. Separability is shown to impose constraints on certain locally invariant (scalar) functions of x , which are derived from local higher-order correlations of the data's velocity $\dot{x}$. The data are separable if and only if they satisfy these constraints, and, if the constraints are satisfied, the sources can be explicitly constructed from the data. The method is illustrated by using it to separate two speech-like sounds recorded with a single microphone.

Given a time series of multicomponent measurements of an evolving stimulus, nonlinear blind source separation (BSS) usually seeks to find a "source" time series, comprised of statistically independent combinations of the measured components. In this paper, we seek a source time series that has a phase-space density function equal to the product of density functions of individual components. In an earlier paper, it was shown that the phase space density function induces a Riemannian geometry on the system's state space, with the metric equal to the local velocity correlation matrix of the data. From this geometric perspective, the vanishing of the curvature tensor is a necessary condition for BSS. Therefore, if this data-derived quantity is non-vanishing, the observations are not separable. However, if the curvature tensor is zero, there is only one possible set of source variables (up to transformations that do not affect separability), and it is possible to compute these explicitly and determine if they do separate the phase space density function. A longer version of this paper describes a more general method that performs nonlinear multidimensional BSS or independent subspace separation.

We have developed an interactive geometric method for 3D reconstruction of the coronary arteries using multiple single-plane angiographic views with arbitrary orientations. Epipolar planes and epipolar lines are employed to trace corresponding vessel segments on these views. These points are utilized to reconstruct 3D vessel centerlines. The accuracy of the reconstruction is assessed using: (1) near-intersection distances of the rays that connect x-ray sources with projected points, (2) distances between traced and projected centerlines. These same two measures enter into a fitness function for a genetic search algorithm (GA) employed to orient the angiographic image planes automatically in 3D avoiding local minima in the search for optimized parameters. Furthermore, the GA utilizes traced vessel shapes (as opposed to isolated anchor points) to assist the optimization process. Differences between two-view and multiview reconstructions are evaluated. Vessel radii are measured and used to render the coronary tree in 3D as a surface. Reconstruction fidelity is demonstrated via (1) virtual phantom, (2) real phantom, and (3) patient data sets, the latter two of which utilize the GA. These simulated and measured angiograms illustrate that the vessel center-lines are reconstructed in 3D with accuracy below 1 mm. The reconstruction method is thus accurate compared to typical vessel dimensions of 1-3 mm. The methods presented should enable a combined interpretation of the severity of coronary artery stenoses and the hemodynamic impact on myocardial perfusion in patients with coronary artery disease.