The prehospital electrocardiogram in suspected acute coronary syndrome
Bibliographic record
Abstract
Acute coronary syndrome (unstable angina and myocardial infarction) is associated most often with a narrowed coronary artery, leading to inadequate blood perfusion (ischemia) of cardiac muscle.Timely diagnosis and treatment is important.For acute myocardial infarction with an elevated ST-segment (STEMI) on an electrocardiogram (ECG), rapid reperfusion therapy is critical for survival.Prehospital ECG strategies are being implemented across Quebec to reduce STEMI treatment delay, but ambulance personnel in this province are not permitted to interpret ECGs.The objectives of this thesis were (1) to examine the diagnostic performance of computerized prehospital ECG interpretation;(2) to estimate the additional time spent "on scene" to acquire prehospital ECGs; and (3) to examine the similarities and differences in information provided by pairs of prehospital and initial in-hospital ECGs.The thesis used data on 1560 patients served by the Urgences-santé ambulance operator in 2005-2006 in metropolitan Montreal-Laval.Using a Bayesian latent class model, the statistical analysis was unique in the literature in considering data from three tests simultaneously (ECG reading by computer and by cardiologists, and hospital diagnosis) and assuming all were imperfect.Sensitivity and specificity of the computer for detection of true ST-segment elevation on the prehospital ECG were estimated as 78.8% (95% credible interval: 68.6-87.3%)and 98.9% (98.2-99.4%),respectively.Sensitivity and specificity for detection of true STEMI were estimated as 69.2% (59.1-78.2%)and 98.9% (98.1-99.4%),respectively; estimated prevalence of STEMI was 9.0% (7.0-11.4%).Positive and negative predictive values for STEMI were estimated as 85% (76-91%) and 97% (96-98%), respectively.In multivariate regression analysis, younger age was the only patient factor associated with
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".