Comparing physical assessment with administrative data for detecting pressure ulcers in a large Canadian academic health sciences centre
Bibliographic record
Abstract
OBJECTIVE: This study aimed to compare classification of pressure ulcers from administrative data with a gold standard assessment, specifically; pressure ulcers confirmed by an independent physical assessment performed by trained nurse surveyors. SETTING: A retrospective analysis of pooled cross-sectional samples of inpatients assessed across 3 consecutive prevalence surveys in a large academic health sciences centre between 2012 and 2013. PARTICIPANTS: There were 2001 patients for whom physical and chart assessments were completed, and for whom a discharge abstract was also available at the time of analysis. The cohort's mean age was 65 years and 55% were women. RESULTS: Based on the physical assessment findings, 14.6% of patients (n=292) had at least 1 pressure ulcer, with a total of 345 pressure ulcers documented among these patients: (stage I=162; stage II=120; stage III=22; stage IV=22 and unstageable=19). Based on coded information, 78 (3.9%) of patients had a pressure ulcer. Of patients with a pressure ulcer determined by the physical assessment, only 21% also had a pressure ulcer captured in the administrative data. Furthermore, only 6% of the patients with a hospital-acquired pressure ulcer, stage II or greater determined by the physical assessment were coded in the Discharge Abstract Database (DAD). CONCLUSIONS: The results of this study demonstrate that coding in the DAD may under-report and fail to accurately reflect the true burden of pressure ulcers in hospitalised patients. This may occur because the presence of pressure ulcers is currently documented in the health record by nurses and not by physicians, yet the administrative data recorded in the DAD only includes physician documented pressure ulcers. We recommend enhancements to the coding methods to monitor and report on pressure ulcers.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".