Sex and gender in perioperative cardiovascular research: protocol for a scoping review
Bibliographic record
Abstract
BACKGROUND: The inadequate inclusion of sex and gender in medical research has resulted in biased clinical guidance and disparities in knowledge and patient outcomes. Despite efforts by regulatory and funding agencies, opportunities to generate sex-specific knowledge are frequently overlooked. While certain disciplines in cardiovascular medicine have made notable progress, these advances have yet to permeate the literature on perioperative cardiovascular complications in non-cardiac surgery. Prompted by the recent findings on sex-specific perioperative cardiovascular outcomes, this review aims to scope the literature in this field and categorize methodological approaches used to incorporate sex and gender in studies of this patient population. METHODS: Joanna Briggs Institute (JBI) methodology for scoping reviews will be followed in stages elaborated by Levac (2010). A comprehensive search strategy will be used to identify relevant primary research published since 2010. Screening will be performed by independent reviewers using predefined inclusion and exclusion criteria. Data will be extracted from full text and supplementary materials of selected articles. Results will be presented as proportions of studies reporting sex and gender, the assigned purpose of these variables in analysis, and where they are reported in the article. In addition, articles will be mapped to the source, country of origin, and year of publication. Narrative summaries will be provided to outline key findings and assess the depth of the literature within each of the major topics (risk assessment/prediction, diagnosis, treatment, prognosis, and outcomes). DISCUSSION: Increasing recognition of the profound and complex implications of sex and gender in medicine has fuelled calls for greater attention to participation equity, sex-specific analysis and reporting. Focusing on perioperative cardiovascular complications, this review has the potential to identify knowledge gaps for future research, as well as areas of strength that could support formal knowledge synthesis or secondary analysis of data from past research. SCOPING REVIEW REGISTRATION: Submitted on August 15th, 2023 (Web of Science osf.io/u25sf).
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.113 | 0.117 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.081 | 0.016 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".