Evaluation of needs and treatment benefits in outpatient care for leg ulcer patients: a pilot study
Bibliographic record
Abstract
OBJECTIVE: Leg ulcers can take a long time to heal and have a high recurrence rate. This study aims to describe the sociodemographic and medical profile, and therapeutic needs of patients with leg ulcers, and the benefits of care in a specialised leg ulcers outpatient clinic. METHOD: This is a descriptive, cross-sectional pilot study of patients of a university hospital outpatient clinic. A sociodemographic and medical questionnaire and the Patient Benefit Index-wound (PBI-w) were used to collect data on the therapeutic needs (patient needs questionnaire, PNQ) and benefits of treatment (patient benefit questionnaire, PBQ) they received. RESULTS: A total of 32 patients with leg ulcers were recruited. Results demonstrated that a clear diagnosis and therapy, rapid wound closure and confidence in the therapy given are the most important treatment objectives for patients. Patients mentioned that their therapy benefitted most from confidence in the treatment given, decreased pain and being able to continue living normally. The PBI-w mean score was 2.93 (standard deviation=0.75) on a scale of zero ('did not help at all') to four ('helped a lot'). The PBI-w score showed that the patients benefitted from the treatment they received. CONCLUSION: This pilot study showed the feasibility of using the PBI-w in practice in an outpatient clinic to assess patients' needs, which could help health professionals improve treatment and care for people with leg ulcers. The study also pointed towards the benefits of care for patients who consult specialised outpatient clinics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".