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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".