Sex differences in neurology: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Sex and gender are independently important in health and disease but have been incompletely explored in neurology. This is in part contributed to by the pre-existing male bias in scientific literature that results in fewer females being included in clinical research and the often interchangeable use of sex and gender in the literature. This scoping review intends to identify the advances as well as under-explored aspects of this field to provide a road map for future research. This paper outlines the methods for a scoping review of published, peer-reviewed literature on sex and gender differences in four subspecialty areas of neurology: demyelination, stroke, epilepsy and headache. METHODS AND ANALYSIS: A detailed search strategy will be used to search five databases pertaining only to sex differences. Specific inclusion and exclusion criteria will be applied to capture relevant literature published from 2014 to 2020. Data will be collected and synthesised to provide an overview of information retrieved, a narrative synthesis of each subspecialty area and map of results. ETHICS AND DISSEMINATION: Research ethics board approval was not required for this study. This study will aid in mapping recent trends in sex differences in four major neurological conditions and will help identify areas for further research. A manuscript will be compiled for publication and presentations of findings. REGISTRATION DETAILS: The final protocol is registered with the Open Science Framework (https://osf.io/n937x/).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.109 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.079 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".