Sex Differences in Symptom Presentation in Acute Coronary Syndromes: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
Background Timely recognition of patients with acute coronary syndromes (ACS) is important for successful treatment. Previous research has suggested that women with ACS present with different symptoms compared with men. This review assessed the extent of sex differences in symptom presentation in patients with confirmed ACS. Methods and Results A systematic literature search was conducted in PubMed, Embase, and Cochrane up to June 2019. Two reviewers independently screened title-abstracts and full-texts according to predefined inclusion and exclusion criteria. Methodological quality was assessed using the Newcastle-Ottawa Scale. Pooled odds ratios (OR) with 95% CI of a symptom being present were calculated using aggregated and cumulative meta-analyses as well as sex-specific pooled prevalences for each symptom. Twenty-seven studies were included. Compared with men, women with ACS had higher odds of presenting with pain between the shoulder blades (OR 2.15; 95% CI, 1.95-2.37), nausea or vomiting (OR 1.64; 95% CI, 1.48-1.82) and shortness of breath (OR 1.34; 95% CI, 1.21-1.48). Women had lower odds of presenting with chest pain (OR 0.70; 95% CI, 0.63-0.78) and diaphoresis (OR 0.84; 95% CI, 0.76-0.94). Both sexes presented most often with chest pain (pooled prevalences, men 79%; 95% CI, 72-85, pooled prevalences, women 74%; 95% CI, 72-85). Other symptoms also showed substantial overlap in prevalence. The presence of sex differences has been established since the early 2000s. Newer studies did not materially change cumulative findings. Conclusions Women with ACS do have different symptoms at presentation than men with ACS, but there is also considerable overlap. Since these differences have been shown for years, symptoms should no longer be labeled as "atypical" or "typical."
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".