Bibliographic record
Abstract
Coronary heart disease (CHD) is the leading cause of morbidity and mortality in both men and women in the developed countries. Despite this fact, females are still under-represented in the majority of clinical trials. At the present time, only limited evidence is available with respect to the female-specific aspects of pathogenesis, management, and outcomes in acute coronary syndrome (ACS). Women less frequently undergo coronary intervention, and a lower proportion of women receive evidence-based pharmacotherapy, compared with men. It has been shown that women benefit from an invasive approach and coronary intervention in ACS as much as men, despite their advanced age and higher rate of bleeding complications. Also, administration of beta-blockers, ACE-inhibitors, and intensive statin therapy is associated with a comparable reduction of cardiovascular event rates in women and men. On the other hand, women may profit less than men from fibrinolytic or glycoprotein IIb/IIIa inhibitor therapy. Both sexes benefit equally from aspirin therapy, whereas contradictory data are available on the efficacy of clopidogrel in women. There is an urgent need for intensive research in the development of female-specific therapeutic strategy in ACS, even though the detailed mechanisms of sex differences are still unknown.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".