Prehospital cooling to improve successful targeted temperature management after cardiac arrest: A randomized controlled trial
Bibliographic record
Abstract
RATIONALE: Targeted temperature management (TTM) improves survival with good neurological outcome after out-of-hospital cardiac arrest (OHCA), but is delivered inconsistently and often with delay. OBJECTIVE: To determine if prehospital cooling by paramedics leads to higher rates of 'successful TTM', defined as achieving a target temperature of 32-34°C within 6h of hospital arrival. METHODS: Pragmatic RCT comparing prehospital cooling (surface ice packs, cold saline infusion, wristband reminders) initiated 5min after return of spontaneous circulation (ROSC) versus usual resuscitation and transport. The primary outcome was rate of 'successful TTM'; secondary outcomes were rates of applying TTM in hospital, survival with good neurological outcome, pulmonary edema in emergency department, and re-arrest during transport. RESULTS: 585 patients were randomized to receive prehospital cooling (n=279) or control (n=306). Prehospital cooling did not increase rates of 'successful TTM' (30% vs 25%; RR, 1.17; 95% confidence interval [CI] 0.91-1.52; p=0.22), but increased rates of applying TTM in hospital (68% vs 56%; RR, 1.21; 95%CI 1.07-1.37; p=0.003). Survival with good neurological outcome (29% vs 26%; RR, 1.13, 95%CI 0.87-1.47; p=0.37) was similar. Prehospital cooling was not associated with re-arrest during transport (7.5% vs 8.2%; RR, 0.94; 95%CI 0.54-1.63; p=0.83) but was associated with decreased incidence of pulmonary edema in emergency department (12% vs 18%; RR, 0.66; 95%CI 0.44-0.99; p=0.04). CONCLUSIONS: Prehospital cooling initiated 5min after ROSC did not increase rates of achieving a target temperature of 32-34°C within 6h of hospital arrival but was safe and increased application of TTM in hospital.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".