Effect of Weekly Specialized Surgeon-led Bedside Wound Care Teams on Pressure Ulcer Time-to-heal Outcomes: Results From a National Dataset of Long-term Care Facilities.
Bibliographic record
Abstract
INTRODUCTION: Delayed healing of pressure ulcers (PUs) in long-term care facilities (LTCFs) is associated with increased morbidity and expense. OBJECTIVE: The authors hypothesize that guideline-based, weekly coordinated care using specialized wound care surgeon-led bedside teams (SLBTs) may improve PU time-to-heal (TTH) outcomes when compared with usual care (UC). MATERIALS AND METHODS: Using a deidentified United States nationwide database, the authors retrospectively compared TTH outcomes of PUs diagnosed in LTCFs treated by either weekly SLBTs or UC. The SLBTs included an external specialized wound care surgeon (with or without a physician assistant and nurse practitioner) collaborating with facility nurses. Usual care was defined as all patient encounters not known to incorporate this team process. Variables assessed included patient age, gender, and comorbidities. The primary outcome measure was TTH; the TTH outcomes then were compared graphically and statistically between groups. Statistical significance was double-sided P ⟨ .05. RESULTS: In 2014, there were 39 459 consecutive PUs treated by UC and 5985 by SLBTs. The 5985 SLBT wounds originated from 3435 patients in 10 states and all geographic regions (mean age, 76.6 years; 55.9% female; 42.8% with hypertension; 23.7% with diabetes). The mean TTH for wounds managed by SLBTs was 47.5 days (median, 21 days) versus 69.0 days (median, 28 days) for wounds managed by UC, corresponding to an absolute TTH decrease of 21.5 days in wounds managed by SLBTs versus UC. Wounds managed by SLBTs also were significantly more likely to heal in less than 28 days (P ⟨ .0001). CONCLUSIONS: Pressure ulcers managed by coordinated nursing and weekly SLBTs appear to heal significantly faster than wounds managed by UC. Further studies are required to confirm these hypothesis-generating results.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".