The electrocardiogram in hypothermia
Bibliographic record
Abstract
OBJECTIVE: Hypothermia is known to adversely affect the electrocardiogram (ECG) in many cases. This study set out to determine the incidence of defined cardiac dysrhythmias, J waves, and conduction abnormalities in urban hypothermia. METHODS: A prospective, multicenter study was carried out to determine the incidence of defined cardiac rhythms in patients suffering from accidental urban hypothermia. The ECGs were independently analyzed by 2 of the authors and placed into 1 of 6 rhythm categories. RESULTS: Seventy-three ECGs were analyzed. Normal sinus rhythm was the most common rhythm (41%). Overall mortality was 36% (26/73). J waves occurred in 36% of survivors and 38% of non-survivors and were, therefore, not prognostic. Shivering artifact was present in 66% of survivors and 38% of nonsurvivors. Although there was no statistically significant association between J waves and survival (P = .21), the presence of shivering artifact was associated with survival in severe hypothermia (P = .047). Atrial fibrillation and junctional bradycardia were both associated with high mortality. CONCLUSIONS: This study confirms that the ECG is abnormal in the majority of patients suffering from accidental hypothermia. J waves do not appear to be independently prognostic in hypothermia. The results suggest that the inability to mount a shivering response may be associated with a poorer outcome; this finding requires further study.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".