Understanding venous leg ulcer pain: results of a longitudinal study.
Bibliographic record
Abstract
Venous leg ulcer pain experienced during compression bandaging is poorly understood. A prospective, pilot cohort study was initiated to determine the feasibility of conducting a large-scale, repeated measures cohort study of venous leg ulcer pain and to document and describe the venous leg ulcer pain experience during the first 5 weeks of treatment with compression bandages. Eligible individuals admitted to a nurse-led community leg ulcer service in one Canadian community were recruited for the 5-week study. Pain assessment tools (ie, numerical rating scale and short form McGill Pain Questionnaire) were evaluated by 20 venous ulcer patients (mean age = 73.7 years) and their nurses for ease of use during one baseline and five weekly follow-up visits. Health-related quality of life (HRQL) information was obtained. Nurses reported on ease of integrating pain data collection into regular clinical care. Each pain assessment tool was audited for completion. Most participants found the pain assessment tools easy to use, but nurses reported lengthened visit times with some participants as a result of tool administration difficulties, particularly the visual analogue scale (VAS). Overall completeness of pain assessment tools ranged from 85.0% (visual analogue scale) to 96.3% (present pain intensity and word descriptor list). The vast majority of patients (18) reported ulcer pain at baseline. Total mean scores for all pain assessment tools used decreased over time, but most patients reported pain throughout the study. The most common pain descriptors used were "aching," "stabbing," "sharp," "tender," and "tiring." Health-related quality of life was low and did not change during the 5-week study. The results of this study suggest that the vast majority of venous ulcer patients experience pain and that it is feasible to examine this pain in individuals receiving care in the community over time.
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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".