Leg-ulcer care in the community, before and after implementation of an evidence-based service
Bibliographic record
Abstract
BACKGROUND: Leg ulcers usually occur in older patients, a growing population for which increasing health care resources are required. Treatment is mainly provided in patients' homes; however, patients often receive poorly integrated services in multiple settings. We report the results of a prospective study of a community-based care strategy for leg ulcers. METHODS: International practice recommendations and guidelines were adapted to make a new clinical protocol. The new model, for a dedicated service staffed by specially trained registered nurses, established initial and ongoing assessment time frames and provided enhanced linkages to medical specialists. Data were collected for 1 year before and after implementation; outcome measures included 3-month healing rates, quality of life and resource usage. RESULTS: Three-month healing rates more than doubled between the year before implementation (23% [18/78]) and the year afterward (56% [100/180]). The number of nursing visits per case declined, from a median of 37 to 25 (p = 0.041); the median supply cost per case was reduced from 1923 dollars to 406 dollars (p = 0.005). INTERPRETATION: Reorganization of care for people with leg ulcers was associated with improved healing and a more efficient use of nursing visits.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".