Clinical interpretation of the Uremic Pruritus in Dialysis Patients (<scp>UP</scp>‐Dial) scale: a novel instrument for the assessment of uremic pruritus
Bibliographic record
Abstract
BACKGROUND: The Uremic Pruritus in Dialysis Patients (UP-Dial) scale is valid and reliable for uremic pruritus (UP) assessment. However, it remains unknown how the scores should be interpreted in clinical practices. OBJECTIVES: To establish a clinical interpretation of the UP-Dial by identifying severity cut-off scores according to the disease severity and burden of pruritus. METHODS: This cross-sectional study developed a classification system for the UP-Dial scores using the patient-based anchors method. From May 2012 through January 2017, 697 dialysis patients were screened. Of these, a total of 258 met the criteria for UP and completed the UP-Dial scale and three sets of patient-assessed anchor questions: (i) global UP intensity by visual analogue scale (VAS)-UP, (ii) Dermatology Life Quality Index and (iii) global kidney disease-related quality of life. The cut-off scores were generated based on the kappa (κ) coefficient of agreement and the area under receiver operating characteristic curve (AuROC) statistics. Subgroup analyses were performed to explore associations between patient characteristics and the UP-Dial severity bands. RESULTS: The proposed scores were ≤12 (mild), 13-21 (moderate) and ≥22 (severe) with κ coefficient ranging between 0.39 and 0.46. Assigned scores were associated with all patient-based anchors. The highest association was with the VAS-UP, and AuROC was 0.80 (mild; 95% CI, 0.75-0.86), 0.66 (moderate; 0.60-0.71) and 0.83 (severe; 0.77-0.89). In subgroup analysis according to patient characteristics, we did not find any significant difference. CONCLUSIONS: The estimated UP-Dial severity band can facilitate the interpretation of UP in practice-based research settings and can be used to support treatment decisions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".