BackgroundThe recent Mastitis and Mammary Abscess Management Audit demonstrated widespread variation in the management of breast abscesses across the United Kingdom (UK), with up to one-fifth undergoing surgical drainage rather than image-guided needle aspiration. The impact of these practices on patient's perspective and quality of life is unclear. This study aimed to assess patients' experiences following breast abscess treatment, focusing on treatment modality, cosmesis, breastfeeding and quality of life.MethodsA cross-sectional online survey was conducted between February and August 2024, aimed at UK-wide adult women with a history of breast abscess. Descriptive and thematic analyses were performed using SPSS and NVivo software, with multiple imputation for missing data.ResultsOf 172 participants, half underwent needle aspiration (50.58%), while 23.84% received surgical incision and drainage. Among those undergoing surgery, 68.29% reported prolonged wound healing, 85.37% experienced permanent scarring and a significant negative impact on their breast appearance (p = 0.029). Breastfeeding was disrupted in 58.12%, and 40.17% were unable to resume breastfeeding following treatment. Amongst participants who underwent surgery, 36.5% reported negative impacts on sexual well-being, 31.7% on mental health, and 24.4% on self-confidence. Thematic analysis revealed two major themes: repercussions of the treatment and issues with provision of care, highlighting delays in diagnosis, inadequate breastfeeding support, and negative cosmetic outcomes.DiscussionThis study is the first to investigate patients' experiences of breast abscess management, highlighting significant variability in practice and the association of worse cosmetic and breastfeeding outcomes with surgical treatment. Standardisation of care and improved patient counselling may improve patient experience and outcomes.

Objectives To identify factors influencing unnecessary non-sterile glove use in operating theatres and to estimate how common these factors are across the UK.Design Mixed-methods study using interviews and a cross-sectional survey.Setting Imperial College Healthcare Trust for interviews and nationally across the UK for the survey.Participants 19 interviewees and 329 survey respondents, all clinical staff working in UK operating theatres.Outcome measures Barriers and facilitators to unnecessary non-sterile glove use in operating theatres.Results The findings highlight a combination of key drivers leading to the unnecessary use of non-sterile gloves: (1) lack of prioritisation of sustainability, (2) fears around negative patient outcomes, (3) strong social influences such as norms to use gloves, (4) the absence of clear guidelines and limited training on glove use, (5) availability of alternatives and quality of gloves and (6) beliefs about personal safety and habitual glove use. Respondents also suggested potential intervention strategies.Conclusions 67% of participants reported using gloves unnecessarily. Our findings highlight the role of habitual behaviour, social influences and unclear guidelines in driving this practice. Interventions should address these factors, for example, by clearly communicating when gloves should and should not be worn, encouraging changes to local social norms towards waste reduction, improving access to hand gel and supporting habit change to reduce unnecessary glove use and associated environmental impact.

BACKGROUND:Recent research aims to leverage technology to further understand surgical recovery by using continuous data. Traditional metrics of readmission, flap failure, and patient-reported outcome measures are limited by poor accuracy and subjectivity. We aimed to validate smartphone-derived physical activity data to objectively measure and analyze trends in recovery following deep inferior epigastric perforator (DIEP) flap breast reconstruction. METHODS:A single-center, retrospective study was conducted. Eligible participants who underwent DIEP reconstruction downloaded a bespoke smartphone application, which retrieved data from 1 month preoperatively to 12 months postoperatively. Physical activity was compared and validated against wearable activity monitor data from a previous study. Temporal trends were visualized using mean daily activity values over predefined intervals. Univariable linear regression assessed associations between clinical variables and short-term recovery. RESULTS:Forty-one patients were included in the study. Wearable activity monitor and smartphone datasets (n=10) showed a positive correlation (0.6379, p=0.0105) demonstrating concurrent validity. Analysis of recovery in DIEP patients (n=34) demonstrated a median return to baseline activity at 27 days (IQR 12 days). Physical activity decreased after DIEP, with mean daily activity dropping to 18% of baseline in the first 2 weeks (SD=11%, p<0.0001) before improving to 107% at 8-12 weeks (SD=78%, p=0.9999). Immediate postoperative reconstruction (p=0.046) and lack of postoperative complications (p=0.0063), were short-term predictors of physical activity. CONCLUSION:This study validates smartphone physical activity as an objective recovery metric in DIEP reconstruction. Future applications include developing recovery prediction models for shared decision-making, implementing perioperative interventions, and postoperative monitoring.

Objective:The aim of this study was to determine the impact of margin width and boost radiotherapy on the local recurrence risk of pure ductal carcinoma in situ (DCIS). Methods and analysis:This is a prospectively registered systematic review and meta-analysis reporting relative risk (RR), OR and HR margin width outcomes. Eligible studies included prospective and retrospective case series with defining margin widths and 48 months of minimum follow-up. All patients (100%) received adjuvant whole breast radiotherapy (WBRT). Results:A total of 40 265 patients with pure DCIS in 31 studies were included. ORs and RR were calculated from 15 studies in 12 519 patients, and HRs were calculated from 12 studies in 12 946 patients. Local recurrence was significantly greater with narrower 'close' margins; 0.1-1 mm versus >1 mm in RR (2.88, 95% CI 1.86 to 3.90; p<0.05), OR (4.82, 95% CI 2.45 to 9.48; p<0.05) and HR analysis (1.34, 95% CI 1.01 to 1.67; p<0.05). Compared with margins >2 mm, significantly greater local recurrence was observed in margins 0.1-2 mm in RR (1.72, 95% CI 1.09 to 2.35; p<0.05) and OR (4.43, 95% CI 3.02 to 6.50; p<0.05). Comparing 0.1-1 mm versus >1 mm and 0.1-2 mm versus >2 mm, differences in local recurrence were not statistically significant, once adjusted for boost radiotherapy. Conclusions:In pure DCIS with WBRT, the local recurrence risk reduces as margin width increases up to 2 mm. The strength of the recommendation for a minimum clear margin of 2 mm is limited by a lack of data comparing 1.1-2 mm with >2 mm. The association between recurrence and close margins is not significant following boost radiotherapy, suggesting a possible alternative to re-excision in patients with close margins <2 mm. Systematic review registration:CRD42022308524.

This systematic review analyses advancements in cognitive state recognition from 2010 to early 2024, evaluating 405 relevant articles from an initial pool of 2398 records identified through five databases: Scopus, Engineering Village, Web of Science, IEEE Xplore, and PubMed. Studies were included if they assessed cognitive states using physiological signals and applied machine learning (ML) or deep learning (DL) techniques in practical task settings. The review highlights a pivotal shift from shallow ML to DL approaches for analysing physiological signals, driven by DL’s ability to autonomously learn complex patterns in large datasets. By 2023, DL has become the dominant methodology, though traditional ML techniques remain relevant. Additionally, there has been a move from neuroimaging to multimodal physiological modalities, with the decrease in neuroimaging use reflecting a trend towards integrating various physiological signals for more comprehensive insights. Cognitive state recognition is applied across diverse domains such as the automotive, aviation, maritime, and healthcare industries, enhancing performance and safety in high-stakes environments. Electrocardiogram (ECG) is the most utilised modality, with convolutional neural networks (CNNs) being the primary DL approach. The trend in cognitive state recognition research is moving towards integrating ECG signals with CNNs and adopting privacy-preserving methodologies like differential privacy and federated learning, highlighting the potential of cognitive state recognition to enhance performance, safety, and innovation across various real-world applications.

Objective:To systematically review technologies that objectively measure cognitive workload (CWL) in surgery, assessing their psychometric and methodological characteristics.Background:Surgical tasks involving concurrent clinical decision-making and the safe application of technical and non-technical skills require a substantial cognitive demand and resource utilization. Cognitive overload leads to impaired clinical decision-making and performance decline. Assessing CWL could enable interventions to alleviate burden and improve patient safety.Methods:Ovid MEDLINE, OVID Embase, the Cochrane Library, and IEEE Xplore databases were searched from inception to August 2023. Full-text, peer-reviewed original studies in a population of surgeons, anesthesiologists or interventional radiologists were considered, with no publication date constraints. Study population, task paradigm, stressor, cognitive load theory (CLT) domain, objective and subjective parameters, statistical analysis, and results were extracted. Studies were assessed for (1) definition of CWL; (2) details of the clinical task paradigm; and (3) objective CWL assessment tool. Assessment tools were evaluated using psychometric and methodological characteristics.Results:A total of 10,790 studies were identified; 9004 were screened; 269 full studies were assessed for eligibility, of which 67 met inclusion criteria. The most widely used assessment modalities were autonomic (32 eye studies and 24 cardiac). Intrinsic workload (eg, task complexity) and germane workload (effect of training or expertize) were the most prevalent designs investigated. CWL was not defined in 30 of 67 studies (44.8%). Sensitivity was greatest for neurophysiological instruments (100% EEG, 80% fNIRS); and across modalities accuracy increased with multisensor recordings. Specificity was limited to cardiac and ocular metrics, and was found to be suboptimal (50% and 66.67%). Cardiac sensors were the least intrusive, with 54.2% of studies conducted in naturalistic clinical environments (higher ecological validity).Conclusions:Physiological metrics provide an accessible, objective assessment of CWL, but dependence on autonomic function negates selectivity and diagnosticity. Neurophysiological measures demonstrate favorable sensitivity, directly measuring brain activation as a correlate of cognitive state. Lacking an objective gold standard at present, we recommend the concurrent use of multimodal objective sensors and subjective tools for cross-validation. A theoretical and technical framework for objective assessment of CWL is required to overcome the heterogeneity of methodological reporting, data processing, and analysis.