Abstract 230: Targeted Temperature Management: Exploring the Association Between Processes of Care and Outcomes After Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Introduction: Randomized trials have demonstrated that Targeted Temperature Management (TTM) improves functional survival in patients after out-of-hospital cardiac arrest (OHCA). However, the optimal process of implementation and delivery remains unclear, specifically how fast, how cold or how long to cool. The objective of this study was to evaluate the association of these processes of care with survival and neurological function in patients after OHCA. METHODS: We conducted a retrospective cohort study on consecutive adult (≥18 years) non-traumatic OHCA patients from November 1, 2007 to January 31, 2012. Eligible patients who were treated with TTM after return of spontaneous circulation were included. The primary and secondary outcomes were in-hospital survival and good neurological status (Modified Rankin Scale of 0-3) at hospital discharge, respectively. Bivariate and generalized estimating equation analyses were used to evaluate the associations between processes of care variables and patient outcomes. Results: There were 5770 consecutive OHCA patients, of whom 747 (12.9%) patients were eligible and received TTM; 365 (48.9%) patients survived to hospital discharge, of whom 241 (66.0%) patients had good neurological outcomes. After adjusting for the Utstein variables, a higher temperature prior to cooling was associated with improved survival [OR 1.26 per °C, 95% CI 1.09-1.46, p=0.002] and neurological outcomes [OR 1.27 per °C, 95% CI 1.08-1.50, p=0.004]. Moreover, a slower rate of cooling was associated with improved survival [OR 0.73 per °C/hr, 95% CI 0.54-1.00, p=0.05] and neurological outcomes [OR 0.74 per °C/hr, 95% CI 0.57-0.97, p=0.03]. The location of cooling initiation, coldest temperature, and cold duration ≤34°C were not associated with outcomes. CONCLUSION: A higher baseline temperature prior to cooling and a slower rate of cooling were associated with both improved survival and neurological outcomes. This may reflect the complex relationship between the extent of brain injury causing hypothalamic dysfunction and impaired thermoregulation, and the cooling techniques used in patients after cardiac arrest.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".