Measuring pain in patients undergoing hemodialysis: a review of pain assessment tools
Bibliographic record
Abstract
BACKGROUND: Patients undergoing hemodialysis frequently report pain with multifactorial causes, not limited to that experienced directly from hemodialysis treatment. Their pain may be nociceptive, neuropathic, somatic or visceral in nature. Despite this, pain in this population remains under-recognized and under-treated. Although several tools have been used to measure pain in patients undergoing hemodialysis as reported in the literature, none of them have been validated specifically in this population. The objective for this review was to compare and contrast these pain assessment tools and discuss their clinical utility in this patient population. METHODS: To identify pain assessment tools studied in patients undergoing hemodialysis, a literature search was performed in PubMed and Medline. An expert panel of dialysis and pain clinicians reviewed each tool. Each pain assessment tool was assessed on how it is administered and scored, its psychometric properties such as reliability, validity and responsiveness to change, and its clinical utility in a hemodialysis population. Brief Pain Inventory, McGill Pain Questionnaire, Pain Management Index, Edmonton Symptom Assessment System, Visual Analogue Scale and Faces Pain Scale were evaluated and compared. RESULTS: This assessment will help clinicians practicing in nephrology to determine which of these pain assessment tools is best suited for use in their individual clinical practice.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".