Targeted temperature management after out-of-hospital cardiac arrest: who, when, why, and how?
Bibliographic record
Abstract
OBJECTIVE: To provide a succinct review of the evidence, framed for the emergency department clinician, for the application of targeted temperature management (TTM) for patients after out-of-hospital cardiac arrest (OHCA). SOURCES OF INFORMATION: MEDLINE, EMBASE, and the Cochrane database were searched for prospective and retrospective studies relevant to the indications of TTM, optimal timing of TTM initiation, method of cooling, and target temperature. MAIN MESSAGE: Two prospective interventional trials reported improved neurologically intact survival with the use of TTM (goal temperatures of 32°C to 34°C) compared with no temperature management in comatose OHCA patients with shockable initial cardiac arrest rhythms. A more recent, high-quality randomized controlled trial including OHCA patients with shockable and nonshockable initial rhythms compared TTM at 33°C versus TTM at 36°C. Despite the study being well powered, superiority of one target temperature over the other was not demonstrated. The benefit of TTM in patients with initial nonshockable rhythms is not clear; however, some observational studies have suggested benefit. There is no evidence that any particular method of temperature regulation is superior. The relationship between time and TTM initiation has not been well established. CONCLUSION: Targeted temperature management, with a target temperature between 32°C and 36°C, as a component of comprehensive critical care is a beneficial intervention for comatose patients with return of spontaneous circulation after OHCA.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".