Understanding pain and quality of life for patients with chronic venous ulcers.
Bibliographic record
Abstract
UNLABELLED: Aim. To identify the impact of pain on quality of life (QOL) of patients with chronic venous ulcers. METHODS: A cross-sectional study was performed on 40 outpatients with chronic venous ulcers who were recruited at one outpatient care center in São Paulo, Brazil. WHOQOL-Bref was used to assess QOL, the McGill Pain Questionnaire-Short Form (MPQ) to identify pain characteristics, and an 11-point numerical pain rating scale to measure pain intensity. Kruskall-Wallis or ANOVA test, with post-hoc correction (Tukey test) was applied to compare groups. Multiple linear regression models were used. RESULTS: The mean age of the patients was 67 ± 11 years (range, 39-95 years), and 26 (65%) were women. The prevalence of pain was 90%, with worst pain mean intensity of 6.2 ± 3.5. Severe pain was the most prevalent (21 patients, 52.5%). Pain most frequently reported was sensory-discriminative and evaluative in quality. Pain was significantly and negatively correlated with physical (PY), environmental (EV), and overall QOL. Compared to a no-pain group, those with pain had lower overall QOL. On multiple analyses, pain remained as a predictor of overall QOL (b = -0.73, P = 0.03) and was also predictive of social QOL, whereas pain did not have any impact on physical, emotional, or social relationships QOL (b = -3.85, P = 0.00) when adjusted for age, number, duration and frequency of wounds, pain dimension (MPQ), partnership, and economic status. CONCLUSION: To improve QOL of outpatients with chronic venous ulcers, the qualities and the intensity of pain must be considered differently.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".