Resource and Infrastructure-Appropriate Management of ST-Segment Elevation Myocardial Infarction in Low- and Middle-Income Countries
Bibliographic record
Abstract
The 143 low- and middle-income countries (LMICs) of the world constitute 80% of the world's population or roughly 5.86 billion people with much variation in geography, culture, literacy, financial resources, access to health care, insurance penetration, and healthcare regulation. Unfortunately, their burden of cardiovascular disease in general and acute ST-segment-elevation myocardial infarction (STEMI) in particular is increasing at an unprecedented rate. Compounding the problem, outcomes remain suboptimal because of a lack of awareness and a severe paucity of resources. Guideline-based treatment has dramatically improved the outcomes of STEMI in high-income countries. However, no such focused recommendations exist for LMICs, and the unique challenges in LMICs make directly implementing Western guidelines unfeasible. Thus, structured solutions tailored to their individual, local needs, and resources are a vital need. With this in mind, a multicountry collaboration of investigators interested in LMIC STEMI care have tried to create a consensus document that extracts transferable elements from Western guidelines and couples them with local realities gathered from expert experience. It outlines general operating principles for LMICs focused best practices and is intended to create the broad outlines of implementable, resource-appropriate paradigms for management of STEMI in LMICs. Although this document is focused primarily on governments and organizations involved with improvement in STEMI care in LMICs, it also provides some specific targeted information for the frontline clinicians to allow standardized care pathways and improved outcomes.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".