Dilemmas in measuring and using pressure ulcer prevalence and incidence: an international consensus
Bibliographic record
Abstract
Pressure ulcer prevalence and incidence data are increasingly being used as indicators of quality of care and the efficacy of pressure ulcer prevention protocols. In some health care systems, the occurrence of pressure ulcers is also being linked to reimbursement. The wider use of these epidemiological analyses necessitates that all those involved in pressure ulcer care and prevention have a clear understanding of the definitions and implications of prevalence and incidence rates. In addition, an appreciation of the potential difficulties in conducting prevalence and incidence studies and the possible explanations for differences between studies are important. An international group of experts has worked to produce a consensus document that aims to delineate and discuss the important issues involved, and to provide guidance on approaches to conducting and interpreting pressure ulcer prevalence and incidence studies. The group's main findings are summarised in this paper.
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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 | 0.438 | 0.398 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.022 | 0.015 |
| Research integrity | 0.021 | 0.037 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".