Validation of the interRAI Pressure Ulcer Risk Scale in Acute Care Hospitals
Bibliographic record
Abstract
OBJECTIVES: To validate the Pressure Ulcer Risk Scale (PURS) to screen for pressure ulcer (PU) outcomes in the acute hospital setting. DESIGN: Secondary data analysis was undertaken using a combined dataset from three prospective cohort studies. SETTING: General medical, surgical, and orthopedic wards in 11 hospitals in two states of Australia. PARTICIPANTS: Individuals aged 70 and older admitted to the hospital for longer than 48 hours from July 2005 to May 2010 (N = 1418). Individuals in coronary or intensive care units, palliative care, or transferred out of the ward within 24 hours were excluded. MEASUREMENTS: Trained nurses used the international Resident Assessment Instrument (interRAI) Acute Care (AC) assessment tool to collect data at admission and discharge. Adverse outcomes were documented on daily ward visits. The PURS was calculated from interRAI items, and its association with PU outcomes was tested using the c-statistic (area under the receiver operator characteristic curve). RESULTS: Complete data were available for 1,371 (96.7%) participants, 85 of whom (6.2%) had a PU at admission. Of the 1,286 without PUs at admission, 42 (3.3%) developed a new PU during their hospital stay. The association between PURS and outcomes had a c-statistic of 0.81 (standard error (SE) 0.02) for prevalent ulcers at admission and 0.70 (SE 0.04) for incidence of new PUs. CONCLUSION: When derived from the interRAI AC tool, the PURS demonstrated good to strong ability to screen for PU outcome in acute care. Assessment burden is reduced without loss of fidelity by integrating the risk scale into an existing assessment system.
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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 | 0.033 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".