Therapeutic hypothermia for out-of-hospital cardiac arrest: An analysis comparing cooled and not cooled groups at a Canadian center
Bibliographic record
Abstract
BACKGROUND: Out of hospital cardiac arrest is a devastating event and is associated with poor outcomes; however, therapeutic hypothermia (TH) is a novel treatment which may improve neurological outcome and decrease mortality. Despite this, TH is not uniformly implemented across Coronary Care and Intensive Care Units in Canada. OBJECTIVE: The purpose of this study was to compare cerebral recovery and mortality rates between patients in our Coronary Care Unit who received TH with a historical control group. MATERIALS AND METHODS: A retrospective chart review was performed of patients admitted to a tertiary care center with out-of-hospital cardiac arrest. Twenty patients who were admitted and cooled after December 2006 were compared with 29 noncooled patients admitted in the 5 years prior as a historical control group. The primary outcomes of interest were in-hospital mortality and neurological outcome. RESULTS: Eleven of 20 (11/20, 55%) patients who were cooled as per protocol survived to hospital discharge, all having a good neurological outcome. Eleven of 29 (11/29, 38%) noncooled patients survived to hospital discharge (Odds Ratio: 0.50, 95% CI: 0.16- 1.60, P=0.26). Eleven of 20 patients who were cooled had a good neurological outcome (CPS I-II, 11/20, 55%), versus 7 of 29 (7/29, 24%) of noncooled patients (Odds ratio: 3.84, 95% CI: 1.13- 13.1, P=0.03). One hundred percent (11/11) of survivors in the cooled group had a good neurological outcome. CONCLUSION: In our center, the use of TH in out-of-hospital cardiac arrest survivors was associated with improved neurological outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".