A retrospective audit to examine the effectiveness of preoperative warming on hypothermia in spine deformity surgery patients
Bibliographic record
Abstract
BACKGROUND: Hypothermia (core body temperature <36°C) during surgery has been associated with surgical site infection, a major risk in all spine deformity surgeries. Forced air warming is an important method of intraoperative temperature maintenance in children. In mid-2010, we empirically introduced preoperative warming as a strategy to reduce intraoperative hypothermia. OBJECTIVE: We report the prevalence and extent of hypothermia during spine deformity surgeries at our institution and evaluate the effect of the introduction of preoperative warming. METHODS: We performed a retrospective audit of temperature data in children who underwent spine deformity surgeries during two-seven-month periods: November 2011 to June 2012 and 2 years prior to this period (before preoperative warming implementation). Specifically, the following data were obtained: (i) case duration; (ii) first measured temperature; (iii) last measured temperature; (iv) percentage of case spent hypothermic; (v) number of hypothermic episodes per case, and (vi) delay between case start and time of first temperature measured. Data were compared visually and using the Mann-Whitney U-test. Confidence intervals (CI) were obtained using the Hodges-Lehmann estimator. RESULTS: Preoperative warming reduced the percentage of case duration spent hypothermic by a median of 111.1 min (P < 0.001, 95% CI 77.1-139.9 min). Additionally, it increased the first measured temperature by a median of 0.5°C (P < 0.001, 95% CI 0.3-0.7°C). The last temperature at the end of the case remained unchanged (P = 0.57, 95% CI -0.2-0.1°C). CONCLUSION: Preoperative warming of children undergoing spine deformity surgery significantly reduces the percentage of case spent hypothermic, thereby potentially reducing risk of perioperative complications.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".