Development of a Patient-Reported Outcomes Tool to Assess Pain and Discomfort in Autosomal Dominant Polycystic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Pain has been identified as a core outcome for individuals with autosomal dominant polycystic kidney disease (ADPKD), but no disease-specific pain assessment has been developed using current development methodology for patient-reported outcomes (PRO) instruments. We developed and validated an ADPKD-specific pain questionnaire: the ADPKD Pain and Discomfort Scale (ADPKD-PDS). METHODS: Conceptual underpinnings were drawn from literature review, concept elicitation, expert consultation, and measurement performance. In the qualitative analysis phase, concepts were elicited from focus groups of adults with ADPKD, and the resulting draft instrument was refined using cognitive debriefing interviews with individuals with ADPKD. For quantitative analysis, adults with ADPKD completed the draft instrument and other PRO tools in an online survey, and a follow-up survey was conducted 3-4 weeks later. Survey responses were analyzed for item-level descriptive statistics, latent model fit statistics, item discrimination, item- and domain-level psychometric statistics, test-retest reliability, responsiveness to change, and convergent validity. RESULTS: In the qualitative phase, 46 focus groups were conducted in 18 countries with 293 participants. Focus groups described three conceptually distinct types of ADPKD-related pain and discomfort (dull kidney pain, sharp kidney pain, and fullness/discomfort). In the quantitative phase, 298 adults with ADPKD completed the online survey, and 108 participants completed the follow-up survey. After iterative refinement of the instrument, latent variable measurement models showed very good fit (comparative fit and nonnormed fit indices both 0.99), as did item- and domain-level psychometric characteristics. The final ADPKD-PDS contains 20 items assessing pain severity and interference with activities over a 7-day recall period. CONCLUSIONS: The ADPKD-PDS is the first validated tool for systematically assessing pain and discomfort in ADPKD.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".