Recommendation to Develop Strategies to Increase the Number of ST-Segment–Elevation Myocardial Infarction Patients With Timely Access to Primary Percutaneous Coronary Intervention
Bibliographic record
Abstract
Although evidence suggests that primary percutaneous coronary intervention (PCI) is the preferred reperfusion strategy in the majority of patients with ST-segment-elevation myocardial infarction (STEMI), only a minority of patients with STEMI are treated with primary PCI, and of those, only a minority receive the treatment within the recommended 90 minutes after entry into the medical system. Market research conducted by the American Heart Association revealed that those involved in the care of patients with STEMI recognize the multiple barriers that prevent the prompt delivery of primary PCI and agree that it is necessary to develop systems or centers of care that will allow STEMI patients to benefit from primary PCI. The American Heart Association will convene a group of stakeholders (representing the interests of patients, physicians, emergency medical systems, community hospitals, tertiary hospitals, and payers) and quality-of-care and outcomes experts to identify the gaps between the existing and ideal delivery of care for STEMI patients, as well as the requisite policy implications. Working within a framework of guiding principles, the group will recommend strategies to increase the number of STEMI patients with timely access to primary PCI.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.028 | 0.009 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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".