Fibrinolytic therapy in acute myocardial infarction: time to treatment in Canada.
Bibliographic record
Abstract
BACKGROUND: Fibrinolytic therapy has become the standard therapy for acute myocardial infarction with ST segment elevation. Many clinical trials have established that early administration of therapy correlates with improved outcomes. However, there are very few published data analyzing time to treatment in Canadian hospitals. OBJECTIVES: To examine all time intervals from onset of symptoms to treatment with fibrinolytic therapy in patients in Canada. METHODS: Using the FASTRAK II database, time intervals in 11,574 patients treated with fibrinolytic therapy in 106 contributing institutions across Canada from 1998 to 2000 were studied. Variables contributing to long delays in starting fibrinolytic therapy were analyzed. RESULTS: The mean time from onset of symptoms to arrival at hospital was 162 min (2.7 h). Only 6.3% of patients received fibrinolytic therapy within 1 h of symptom onset. Time from hospital arrival to acquisition of first 12 lead electrocardiogram was 14 min. Mean time from diagnostic electrocardiogram to decision to treat was 29.7 min, and time from decision to treat to administration of fibrinolytic therapy was 11 min. The overall average time from arrival at hospital to administration of fibrinolytic therapy was 69 min. CONCLUSION: In Canada, time from onset of symptoms to hospital presentation precludes early fibrinolytic therapy administration in the majority of cases. Time intervals from arrival in the emergency department to administration of fibrinolytic therapy are longer than the published and accepted standards. Strategies to alter health care seeking behaviour and to minimize in-hospital delays are needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".