Evidence-Based Guidelines for Prevention of Perioperative Hypothermia
Bibliographic record
Abstract
Objective: To appraise the available evidence for patient monitoring, perioperative active warming methods, outcomes supporting the prevention of perioperative hypothermia, and implementation strategies for the prevention of perioperative hypothermia. Outcomes: Outcomes assessed included the precision and accuracy of thermometers, efficacy of warming devices including IV fluid warmers and forced-air devices, and surgical site infections and morbid cardiac events associated with PH. Evidence: MEDLINE, EMBASE, and the Cochrane Database were searched to identify randomized controlled trials of efficacy and prospective studies of diagnostic accuracy. Two authors reviewed the abstracts to identify articles for critical appraisal. The methods of the Canadian Task Force on Preventive Health Care were employed to grade study quality and level of evidence, as well as formulate the final recommendations. Recommendations: The evidence supports the use of esophageal temperature probes for temperature monitoring in all patients undergoing abdominal surgery while under general anaesthetic; awake patients and patients in recovery should have temperatures monitored using
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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".