Point prevalence of wounds in a sample of acute hospitals in Canada
Bibliographic record
Abstract
To provide new information on wound prevalence and the potential resource impact of non healing wounds in the acute sector by summarising results from wound audits carried out at 13 acute hospitals in Canada in 2006 and 2007. Audits were carried out in each hospital by the same independent team of advanced practice nurses using standard data-collection forms. The results reported here were derived from the summary reports for each hospital. A total of 3099 patients were surveyed (median 259 patients per hospital). In the sample hospitals, the mean prevalence of patients with wounds was 41.2%. Most wounds were pressure ulcers (56.2%) or surgical wounds (31.1%). The mean prevalence of pressure ulcers was 22.9%. A majority of pressure ulcers (79.3%) were hospital-acquired, and 26.5% were severe (Stage III or IV). The rate of surgical wound infection was 6.3%. Forty-five percent of patients had dressings changed at least daily and the mean dressing time was 10.5 minutes. Wounds are a common and potentially expensive occurrence in acute hospitals. Any wound has the potential to develop complications which compromise patient safety and increase hospital costs. Ensuring consistent, best-practice wound management programmes should be a key priority for hospital managers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".