Gender Differences in Clinical Practice Regarding Coronary Heart Disease: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: A systematic review was performed with the aim of analysing potential sex differences in the overall treatment of coronary heart disease (CHD). Methods: Studies published between January 2011 and November 2023 that conducted a sex-based analysis of the provision of any type of therapeutic measure to treat CHD were included. A search was performed of the Web of Science database in November 2023, resulting in 9070 articles. Study quality was examined using the Newcastle–Ottawa scale. A worksheet was produced to extract data pertaining to the title, year of publication, sample, context, study design, dependent variables, time-frame, treatment type, and outcomes reported by each article. This systematic review followed PRISMA guidelines, and the research protocol was submitted to PROSPERO (CRD42022330238). Results: A total of 80 articles presenting data representing 560.070,624 individual datapoints were selected to comprise the final sample. The main findings revealed that the majority of studies highlighted inequalities that disadvantaged females in all analysed treatment categories (pharmacological treatment, invasive interventions, rehabilitation programmes, and other treatment types). Conclusions: Despite the abundance of evidence on the need to improve healthcare provision to females with CHD, few studies examined the reasons or mechanisms underlying the inequalities identified.
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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".