Decreasing Intraoperative Skin Damage in Prone-Position Surgeries
Bibliographic record
Abstract
OBJECTIVE: To determine if subepidermal moisture (SEM) measures help detect and prevent intraoperative acquired pressure injuries (IAPIs) for prone-position surgery. METHODS: In this clinical trial of patients (n = 39 preintervention, n = 48 intervention, 100 historical control) undergoing prone-position surgery, researchers examined the use of multidimensionally flexible silicone foam (MFSF) dressings applied preoperatively to patients' face, chest, and iliac crests. Visual skin assessments and SEM measures were obtained preoperatively, postoperatively, and daily for up to 5 days or until discharge. Electronic health record review included demographic, medical, and surgery data. RESULTS: Of the 187 total participants, 76 (41%) were women. Participants' mean age was 61.0 ± 15.0 years, and 9.6% were Hispanic (n = 18), 9.6% were Asian (n = 18), 6.9% were Black or African American (n = 13), and 73.8% were White (n = 138). Participants had a mean Scott-Triggers IAPI risk score of 1.5 ± 1.1. Among those with no erythema preoperatively, fewer intervention participants exhibited postoperative erythema on their face and chest than did preintervention participants. Further, fewer intervention participants had SEM-defined IAPIs at all locations in comparison with preintervention participants. The MFSF dressings overcame IAPI risk factors of surgery length, skin tone, and body mass index with fewer IAPIs in intervention participants. CONCLUSIONS: Patients undergoing prone-position surgeries developed fewer IAPIs, and SEM measures indicated no damage when MFSF dressings were applied to sites preoperatively. The SEM measures detected more damage than visual assessment.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".