Response to Letter Regarding Article, “Emergency Department Bypass for ST-Segment–Elevation Myocardial Infarction Patients Identified With a Prehospital Electrocardiogram: A Report From the American Heart Association Mission: Lifeline Program”
Bibliographic record
Abstract
We thank Dr Maier and colleagues for their letter highlighting the importance of networks and systems of care to optimize early ST-segment-elevation myocardial infarction (STEMI) diagnosis and treatment. A system is typically defined as an integrated group of entities coordinating the provision of care within a region. A care system for STEMI includes emergency medical service providers, referral centers/non-percutaneous coronary intervention (PCI) hospital(s), and receiving centers/primary PCI hospital(s). Each party has a predefined action plan based on consensus on how to best implement guideline recommendations with the aim of providing optimal care to the maximum number of eligible patients. Establishing STEMI care systems has been associated with a significant improvement in the overall use and timeliness of reperfusion. 2 In 2009, the American College of Cardiology Foundation/American Heart Association STEMI guidelines added a new Class I recommendation that "each community should develop a STEMI system of care." 3 Recommended system features included regular multidisciplinary team meetings that include emergency medical services, data collection and sharing by non-PCI and PCI hospitals, prehospital STEMI identification and activation, destination protocols for PCI hospitals, and transfer protocols for patients who arrive at non-PCI hospitals.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.030 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".