Improving Outcomes by Implementing a Pressure Ulcer Prevention Program (PUPP): Going beyond the Basics
Bibliographic record
Abstract
A multidisciplinary process improvement program was initiated at the University of Miami Hospital (UMH) in 2009 to identify the prevalence of hospital-acquired pressure ulcers (HAPU) at the institution and to implement interventions to reduce the incidence of HAPU. This deliberate and thoughtful committee-driven process evaluated care, monitored results, and designed evidence-based strategic initiatives to manage and reduce the rate of HAPU. As a result all inpatient beds were replaced with support surfaces, updated care delivery protocols were created, and monitored, turning schedules were addressed, and a wound, ostomy, and continence (WOC) nurse and support staff were hired. These initial interventions resulted in a decrease in the prevalence of HAPU at UMH from 11.7% of stage II to IV ulcers in the second quarter, 2009 to 2.1% the third quarter. The rate remained at or near the 2009 UMH benchmark of 3.1% until the first quarter of 2012 when the rate rose to 4.1%. At that time new skin products were introduced into practice and continuing re-education was provided. The rate of HAPU dropped to 2.76% by the second quarter of 2012 and has remained steadily low at 1%-2% for nine consecutive quarters.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".