Targeted Temperature Management After Cardiac Arrest: The Montreal Heart Institute Experience
Bibliographic record
Abstract
Background Targeted temperature management (TTM) has been associated with an improvement in neurological function and survival in patients with cardiac arrest (CA) and an initially shockable rhythm. We report the Montreal Heart Institute (MHI) experience using TTM to evaluate mortality and neurological outcome in patients remaining in coma after CA, regardless of the initial rhythm. Methods We performed a retrospective review of all patients receiving TTM at the MHI between 2008 and 2015. Primary outcome was a composite of mortality and poor neurological outcome at hospital discharge. We also evaluated the long-term outcomes of those who initially survived to hospital discharge. Results A total of 147 patients (120 men, mean age 59.5 ± 12.5 years) underwent TTM at the MHI during the study period. Overall survival to hospital discharge with good neurological outcome was 45.6%. Shockable rhythm was associated with a better outcome (mortality odds ratio, 0.212; 95% confidence interval, 0.068-0.664; P = 0.008). Of the 11 initial survivors with a poor neurological status (Cerebral Performance Category ≥ 3), 4 died rapidly (within 1 month of hospital discharge), but 6 (54.5%) markedly improved their neurological status to Cerebral Performance Category 1. Long-term survival (mean follow-up of 38 ± 26 months) for those alive at hospital discharge (n = 76 patients) was 81.9%. Conclusion Our retrospective analysis of CA survivors treated with TTM at MHI showed good survival, similar to the published results from the landmark randomized controlled trials, despite enrolling patients with nonshockable rhythms. A significant proportion of survivors with poor neurological outcome at discharge improved at follow-up.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".