Evidence Summary of Temperature Management for Comatose Patients after Cardiopulmonary Resuscitation in ICUs
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Objective: This study aims to select and summarize the best evidence of temperature management for comatose patients after cardiopulmonary resuscitation in intensive care units (ICUs) at home and abroad. Method: Some well-known databases at home and abroad have been searched to find the guidelines, expert consensus, original documents, evidence summaries, and systematic evaluation about temperature management for comatose patients after cardiopulmonary resuscitation in ICUs. The databases included PubMed, Up to Date, Cochrane Library, the website of Registered Nurses' Association of Ontario, the Guideline Library of National Institute for Health and Clinical Excellence of the UK, China National Knowledge Infrastructure (CNKI), Wanfang Database, and VIP. The period for search is from the establishment of each database to the present. Two researchers who have received evidence-based nursing training and passed the examination evaluated, extracted, and integrated the literature quality with a blind method to summarize the best evidence. Results: A total of 10 pieces of literature were included in this study, including 4 in Chinese and 6 in English. Specifically, there were 4 guidelines, 1 expert consensus, 2 evidence summaries, 1 systematic evaluation, 1 literature review, and 1 comparative experiment, accounting for 40.0%, 10.0%, 20.0%, 10.0%, 10.0%, and 10.0%, respectively. The literature and materials were all qualified, and there was no heterogeneity and no significant publication bias in the included literature. The best evidence involved mild hypothermia therapy, rewarming, prevention of mild hypothermia-related complications, and nutritional support, with a total of 21 pieces of evidence (including 11, 3, 5, and 2 pieces of evidence, respectively). In terms of the recommendation grade, 7 pieces of evidence were at Grade A and 14 at Grade B. Conclusion: Health care providers should implement hypothermia management in comatose patients after cardiopulmonary resuscitation in ICUs, pay attention to the prevention of related complications, and provide enteral nutrition support.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".