Pain in Chronic Leg Ulcers
Bibliographic record
Abstract
OBJECTIVES: Describe the characteristics of pain in patients with chronic leg ulcers, correlate these descriptions with the characteristics of the ulcers and with patients' sociodemographic data, and determine the measures used for pain management. DESIGN: Descriptive and cross-sectional study with quantitative approach. SETTING AND SUBJECTS: The convenience sample was composed of 90 patients with chronic leg ulcers drawn from 4 outpatient departments in Sao Paulo and Curitiba. INSTRUMENTS: Intensity and quality of patients' pain were determined using a 0-10 numeric pain intensity rating scale and a short version of the McGill Pain Questionnaire. METHODS: After gaining ethics committee approval, patients were selected during their ambulatory visits and interviewed by researchers. Data obtained were analyzed using the Kolmogorov-Smirnov Normality test, Mann-Whitney test, Chi-Square test, and Spearman's Correlation coefficient. RESULTS: Seventy-three (81.11%) patients had venous ulcers. The mean pain intensity reported was 3.10 (SD = 3.15), the mean intensity of the "worst pain of the week" was 7.56 (SD = 2.96), and the mean intensity of the "best pain of the week" was 2.05 (SD = 2.37). The McGill Questionnaire showed that sensitive descriptors were most frequently used to describe the pain. Statistically significant correlations (P < .05) were observed. The most intense pain was reported in subjects from the lowest income bracket studied and female gender and was correlated with alterations in sleep, movement, walking, and mood. Nonsteroidal antiinflammatory drugs were most commonly used (70%) for pain management. CONCLUSIONS: This study indicates the need for more attention and understanding on the part of healthcare workers regarding leg ulcer pain and its characteristics and the need for specific and effective procedures designed to contribute to the improvement in quality of life of these individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".