Short-Periods of Pre-Warming in Laparoscopic Surgery. A Non-Randomized Clinical Trial Evaluating Current Clinical Practice
Bibliographic record
Abstract
Background: Pre-warming prevents perioperative hypothermia. We evaluated the current clinical practice of pre-warming and its effects on temperature drop and postoperative complications; Methods: This prospective, observational pilot study examines clinical practice in a tertiary hospital on 99 patients undergoing laparoscopic urological surgery. Pre-warming was performed in the pre-anesthesia room. Patients were classified into three groups: P 0 (non-prewarmed), P 5–15 (pre-warming 5–15 min) and P > 15 (pre-warming 15–30 min). Tympanic temperature was recorded in the pre-anesthesia room, prior to anesthesia induction, and in the PACU. Esophageal temperature was recorded intraoperatively. The occurrence of shivering, pain intensity, length of stay in PACU, and postoperative complications during hospital stay were also recorded; Results: After pre-warming, between-group difference in body temperature was higher in P > 15 than in P 0 (0.4 °C, 95% CI 0.14–0.69, p = 0.004). Between P 5–15 and P 0 difference was 0.2 °C (95% CI 0.04–0.55, p = 0.093). Temperature at the end of surgery was higher in pre-warmed groups [mean between-group difference 0.5 °C (95% CI 0.13–0.81, p = 0.007) for P 5–15; 0.9 °C (95% CI 0.55–1.19, p < 0.001) for P > 15]. Pain and shivering was less common in pre-warmed groups. Postoperative transfusions and surgical site infections were lower in P > 15; Conclusion: Short-term pre-warming prior to laparoscopic urological surgery decreased temperature perioperative drop and postoperative complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.127 | 0.552 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".