Abstract 70: External Validation of the Hypothermic to Ischemic Ratio as a Predictor of Survival from Out-of-Hospital Cardiac Arrest for Patients Presenting with a Shockable Rhythm
Bibliographic record
Abstract
Objectives: The Hypothermic to Ischemic Ratio (H/I Ratio = total hypothermic time (THT) / total ischemic time (TIT)) was derived to predict survival after out-of-hospital cardiac arrest (OHCA) and found to be highly significant in multivariable models. We sought to externally validate these findings. Methods: Adult, non-traumatic, OHCA patients presenting with a shockable rhythm (VF/pVT) and surviving to hospital admission between September 1, 2007, and December 31, 2011, were considered for inclusion. Structured data were abstracted from a population-based dataset (Strategies for Post Arrest Care (SPARC) Network database (Toronto, Canada)). THT was measured from initiation in the ED, through therapeutic hypothermia (TH) and rewarming to at least 36.5°C. TIT was estimated from call to 911 or EMS witnessed collapse to return of spontaneous circulation (ROSC). Patients were excluded for age >90 years, TIT <5 or >90 minutes, cooling initiated >6 hours after ROSC, rewarming time >30 hours, or missing relevant intervals. Stepwise logistic regression was used to fit the most parsimonious model. Results: Overall, 1,170 OHCA patients received TH, average age 65 years (SD 15.4), 71% male, 85% witnessed, and 43% with bystander CPR. A total of 497 VF/pVT patients met our criteria, with 52.5% surviving to discharge. In multivariable modeling (Table), TIT, age, and H/I Ratio were the most significant predictors for survival (Chi-Sq 123.43, p<0.0001, R² = 0.25, area under curve = 0.82). Conclusions: External validation of the H/I Ratio confirmed it is a significant predictor for survival in OHCA patients presenting with a shockable rhythm. Prospective studies should consider H/I Ratio as a potential predictor for investigation.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".