A routine electrocardiogram cannot be used to determine the size of myocardial infarction in the rat
Bibliographic record
Abstract
Nine lead electrocardiograms of non-infarcted (N = 61) and infarcted (N = 71) female Wistar rats (200-250 g) were analyzed in order to distinguish left ventricle myocardial infarction (MI) larger than 40% (LMI) from MI smaller than 40% (SMI). MI larger than 40% clearly caused a deviation of AQRS and AT from normal values of 270-360 degrees to 90-270 degrees. Infarcted rats showed Q wave in D1 larger than 1 mm with 94% sensitivity and 100% specificity. The sum of QRS positivity in V1, V2 and V6 lower than 10 mm identified MI with 82% sensitivity and 100% specificity. The data showed that MI can be easily and reliably diagnosed by electrocardiogram in the rat. However, contradicting what is frequently believed, when specificity and sensitivity were analyzed focusing on MI size, none of these current electrocardiographic indices of MI size adequately discriminates LMI from SMI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".