Objectives: To examine the association between doctors' sex and receiving sanctions on their medical registration, while controlling for other potentially confounding variables.

Design: Cross-sectional study.

Setting: The General Medical Council (GMC)'s List of Registered Medical Practitioners (LRMP) database of doctors practising in the UK.

Population: All doctors on the GMC's LRMP on 29 May 2013. The database included all doctors who are or have been registered to practise medicine in the UK since October 2005. The exposure of interest was doctor's sex. Confounding variables included years since primary medical qualification, world region of primary medical qualification and specialty.

Outcome measures Sanctions on a doctor's medical registration. Sanction types included warnings, undertakings, conditions, suspension or erasure from the register. Binary logistic regression modelling, controlling for confounders, described the association between the doctor's sex and sanctions on a doctor's medical registration.

Results Of the 329 542 doctors on the LRMP, 2697 (0.8%) had sanctions against their registration, 516 (19.1%) of whom were female. In the fully adjusted model, female doctors had nearly a third of the odds (OR: 0.37, 95% CI: 0.33 to 0.41) of having sanctions compared to male doctors. There was evidence that the association varies with specialty, with female doctors who had specialised as general practitioners being the least likely to receive sanctions compared with their male colleagues (OR: 0.26, 95% CI: 0.22 to 0.31).

Conclusions Female doctors have reduced odds of receiving sanctions on their medical registration when compared with their male colleagues. This association remained after adjustment for the confounding factors. These results are representative of all doctors registered to practise in the UK. Further exploration of why doctors' sex may impact their professional performance is underway.

Background
The relationship between male sex and poor performance in doctors remains unclear, with high profile studies showing conflicting results. Nevertheless, it is an important first step towards understanding the causes of poor performance in doctors. This article aims to establish the robustness of the association between male sex and poor performance in doctors, internationally and over time.

Methods
The electronic databases MEDLINE, EMBASE, and PsycINFO were searched from inception to January 2015. Backward and forward citation searching was performed. Journals that yielded the majority of the eligible articles and journals in the medical education field were electronically searched, along with the conference and poster abstracts from two of the largest international medical education conferences. Studies reporting original data, written in English or French, examining the association between sex and medico-legal action against doctors were included. Two reviewers independently extracted study characteristics and outcome data from the full texts of the studies meeting the eligibility criteria. Study quality was assessed using the Newcastle-Ottawa scale. A random effect meta-analysis model was used to summarize and assess the effect of doctors' sex on medico-legal action. Extracted outcomes included disciplinary action by a medical regulatory board, malpractice experience, referral to a medical regulatory body, complaints received by a healthcare complaints body, criminal cases, and medico-legal matter with a medical defence organisation.

Results
Overall, 32 reports examining the association between doctors' sex and medico-legal action were included in the systematic review (n=4,054,551), of which 27 found that male doctors were more likely to have experienced medico-legal action. 19 reports were included in the meta-analysis (n=3,794,486, including 20,666 cases). Results showed male doctors had nearly two and a half times the odds of being subject to medico-legal action than female doctors. Heterogeneity was present in all meta-analyses.

Conclusion
Male doctors are more likely to have had experienced medico-legal actions compared to female doctors. This finding is robust internationally, across outcomes of varying severity, and over time.

The discrete and charge-separated nature of matter — electrons and nuclei — results in local electrostatic fields that are ubiquitous in nanoscale structures and relevant in catalysis, nanoelectronics and quantum nanoscience. Surface-averaging techniques provide only limited experimental access to these potentials, which are determined by the shape, material, and environment of the nanostructure. Here, we image the potential over adatoms, chains, and clusters of Ag and Au atoms assembled on Ag(111) and quantify their surface dipole moments. By focusing on the total charge density, these data establish a benchmark for theory. Our density functional theory calculations show a very good agreement with experiment and allow a deeper analysis of the dipole formation mechanisms, their dependence on fundamental atomic properties and on the shape of the nanostructures. We formulate an intuitive picture of the basic mechanisms behind dipole formation, allowing better design choices for future nanoscale systems such as single-atom catalysts.

Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, variation in the appearance of mitotic figures causes a high degree of discordance among pathologists. With advances in deep learning models, several automatic mitosis detection algorithms have been proposed but they are sensitive to domain shift often seen in histology images. We propose a robust and efficient two-stage mitosis detection framework, which comprises mitosis candidate segmentation (Detecting Fast) and candidate refinement (Detecting Slow) stages. The proposed candidate segmentation model, termed EUNet, is fast and accurate due to its architectural design. EUNet can precisely segment candidates at a lower resolution to considerably speed up candidate detection. Candidates are then refined using a deeper classifier network, EfficientNet-B7, in the second stage. We make sure both stages are robust against domain shift by incorporating domain generalization methods. We demonstrate state-of-the-art performance and generalizability of the proposed model on the three largest publicly available mitosis datasets, winning the two mitosis domain generalization challenge contests (MIDOG21 and MIDOG22). Finally, we showcase the utility of the proposed algorithm by processing the TCGA breast cancer cohort (1,124 whole-slide images) to generate and release a repository of more than 620K potential mitotic figures (not exhaustively validated). [Abstract copyright: Copyright © 2024. Published by Elsevier B.V.]