The Impact of Perioperative Warming in an Outpatient Aesthetic Surgery Setting
Bibliographic record
Abstract
BACKGROUND: Perioperative hypothermia can lead to surgical complications, including bleeding, infection, increased patient discomfort, and longer recovery time. Plastic surgeons have become increasingly aware of this important patient safety issue. OBJECTIVES: The authors evaluate the impact of perioperative warming in an outpatient plastic surgery setting. METHODS: A retrospective review was performed of 108 patients who received several simple measures to prevent perioperative hypothermia. Patients dressed in warm clothing and were covered with an electric blanket in both the holding area and the recovery room. Intraoperative interventions included higher ambient room temperature, skin exposure only at the surgical site, forced-air warming, and the use of warmed fluids. This warmed group was compared with a historical control group of 106 patients who underwent plastic surgery in the period immediately before implementation of these measures. Patient demographics and procedural characteristics were similar for the 2 groups. RESULTS: The requirement for intraoperative analgesia was significantly lower for the warmed group (111 vs 125 µg fentanyl in the control group; P = .042). Patients in the warmed group required less time in the recovery room and met discharge criteria sooner (127 vs 141 minutes; P = .001). No significant difference was observed in the incidence of complications. CONCLUSIONS: Simple measures to maintain perioperative normothermia improve patient comfort and recovery following aesthetic surgery. Through a continuous-improvement culture, the authors have successfully implemented warming strategies that prevent perioperative hypothermia and improve surgical outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".