Before the door: Comparing factors affecting symptom onset to first medical contact for STEMI patients between a high and low-middle income country
Bibliographic record
Abstract
Background: Early reperfusion in patients with ST-segment elevation myocardial infarction (STEMI) has been associated with preservation of left ventricular function and decrease in mortality. Symptom onset to first medical contact (FMC) time consumes the majority of total ischemic time, and remains one of the main reasons that patients do not receive timely care. With FMC to reperfusion time being effectively reduced in many parts of the world, the focus is now shifting to reducing symptom onset to FMC times. Methods: This mixed-methods observational study was designed to elucidate factors affecting symptom onset to FMC time at a regional cardiac center in a low-middle income country (LMIC) and a high-income country (HIC). A review of the Aswan Heart Center and Hamilton General Hospital STEMI registry in Egypt and Canada was conducted, and retrospective semi-structured questionnaires carried out for a convenience sample of 158 patients. Results: Gender, symptom type and severity were none-modifiable factors found between early and late presenters. Modifiable factors found were actions of bystanders, actions of patients, transportation method and time. Emotional factors also showed differences between the two groups. Conclusion: While some concepts are generalizable, contextual differences in demographics, risk factors, access and knowledge are identified. These factors can be used to inform tailored knowledge translation strategies to help reduce symptom onset to FMC in both LMIC and HIC.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".