Sex-Specific Cut-Offs for High-Sensitivity Cardiac Troponin: Is Less More?
Bibliographic record
Abstract
Management of patients presenting to the Emergency Department with chest pain is continuously evolving. In the setting of acute coronary syndrome, the availability of high-sensitivity cardiac troponin assays (hs-cTn) has allowed for the development of algorithms aimed at rapidly assessing the risk of an ongoing myocardial infarction. However, concerns were raised about the massive application of such a simplified approach to heterogeneous real-world populations. As a result, there is a potential risk of underdiagnosis in several clusters of patients, including women, for whom a lower threshold for hs-cTn was suggested to be more appropriate. Implementation in clinical practice of sex-tailored cut-off values for hs-cTn represents a hot topic due to the need to reduce inequality and improve diagnostic performance in females. The aim of this review is to summarize current evidence on sex-specific cut-off values of hs-cTn and their application and usefulness in clinical practice. We also offer an extensive overview of thresholds reported in literature and of the mechanisms underlying such differences among sexes, suggesting possible explanations about debated issues.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".