Out-of-hospital cardiac arrest in adults: lowering body temperature.
Bibliographic record
Abstract
INTRODUCTION: Post-resuscitation care after return of spontaneous circulation is critical to improving patient outcomes in sudden cardiac death. Therapeutic hypothermia has been a mainstay of treatment after successful cardiopulmonary resuscitation in the setting of ventricular fibrillation or pulseless ventricular tachycardia. METHODS AND OUTCOMES: We conducted a systematic overview, aiming to answer the following clinical question: What are the effects of lowering body temperature for comatose survivors of out-of-hospital cardiac arrest associated with ventricular tachycardia or ventricular fibrillation? We searched: Medline, Embase, The Cochrane Library, and other important databases up to November 2014 (Clinical Evidence overviews are updated periodically; please check our website for the most up-to-date version of this overview). RESULTS: At this update, searching of electronic databases retrieved 222 studies. After deduplication and removal of conference abstracts, 114 records were screened for inclusion in the overview. Appraisal of titles and abstracts led to the exclusion of 89 studies and the further review of 25 full publications. Of the 25 full articles evaluated, one systematic review included in a previous version was updated and three RCTs were added at this update. We performed a GRADE evaluation for five PICO combinations. CONCLUSIONS: In this systematic overview, we categorised the efficacy for three interventions based on information about the effectiveness and safety of therapeutic hypothermia, different lower body temperatures, and different durations of lower body temperatures.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".