Early or First Aid Administration Versus Late or In-hospital Administration of Aspirin for Non-traumatic Adult Chest Pain: A Systematic Review
Bibliographic record
Abstract
Chest pain is a common symptom of acute coronary syndrome, including myocardial infarction (MI). Treatment with antiplatelet agents, such as aspirin, improves survival, although the ideal dose is uncertain. It is unknown if outcomes can be improved by giving aspirin early in the course of MI as part of the first-aid management as opposed to late or in-hospital administration. We searched the Medline, Embase, and Cochrane databases and used Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) and Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) for determining the certainty of evidence. We included studies in adults with non-traumatic chest pain, where aspirin was administered early (within two hours) following the onset of chest pain as part of first-aid management as compared with late or in-hospital administration (The International Prospective Register of Systematic Reviews (PROSPERO) registration number: CDR153316). From 1470 references, we included three studies (one randomized controlled trial (RCT) and two non-RCTs). Early administration (median 1.6 hours or pre-hospital) was associated with increased survival as compared with late administration (median 3.5 hours or in-hospital) at seven days; risk ratio (RR) 1.04 (95% CI 1.03-1.06), 30 days RR 1.05 (95% 1.02-1.07), and one-year RR 1.06 (95% CI1.03-1.10). The evidence is of very low certainty due to limitations in study design and the imprecision of the evidence. This systematic review would suggest that the early or first-aid administration of aspirin to adults with non-traumatic chest pain improves survival as compared with late or in-hospital administration.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".