The Measurement of Leg Ulcer Pain: Identification and Appraisal of Pain Assessment Tools
Bibliographic record
Abstract
OBJECTIVE: To identify and compare the psychometric, clinical sensibility, and pain-specific properties of leg ulcer pain assessment tools for use as a guide for clinicians and researchers. DESIGN: Pain assessment tools were selected for appraisal based on 4 inclusion criteria: (1) designed specifically to measure either quality and/or intensity of pain, (2) used in at least 2 different diseases and/or pain-inducing interventions in adults, (3) generic, and (4) patient self-reporting. The tools were appraised against psychometric properties, clinical sensibility attributes, and pain-specific issues. Two reviewers independently reviewed each abstract, with a third reviewer resolving any disagreements. Then the first 2 reviewers independently assessed the selected tools using the predetermined appraisal criteria. RESULTS: Of 54 identified pain assessment tools, 5 (the pain ruler, the numerical rating scale, the visual analogue scale, the verbal descriptor scale, and the short-form McGill Pain Questionnaire) met the inclusion criteria. Each tool met the appraisal criteria to varying degrees. CONCLUSIONS: The use of a pain assessment tool to measure leg ulcer pain is recommended. Clinicians must decide independently which factors are most important when selecting a tool. Although a specific pain assessment approach cannot yet be recommended, a 2-step pain assessment process is most practical. To optimize pain management, further study is needed to ensure that leg ulcer pain is accurately and reliably assessed.
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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 | 0.048 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".