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.
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 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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".