Design and Rationale of Health-Related Quality of Life and Patient-Reported Outcomes Assessment in the Frequent Hemodialysis Network Trials
Bibliographic record
Abstract
BACKGROUND: End-stage renal disease patients experience significant impairments in health-related quality of life (HRQOL). Testing various strategies to improve patient HRQOL in multicenter clinical trials, such as the Frequent Hemodialysis Network (FHN) trials is vitally important. AIMS: The aim of this paper is to describe the design and conduct of HRQOL and patient-reported outcomes (PRO) assessment in the FHN trials. METHODS: In the FHN trials, HRQOL was examined as a multidimensional concept, and the SF-36 RAND Physical Health Composite score was one of the co-primary outcomes. The instruments completed to assess HRQOL included the Medical Outcomes Study Short Form SF-36, Health Utilities Index 3, Sleep Problems Index, Beck Depression Inventory and feeling thermometer. These instruments have been shown to have high reliability, validity and responsiveness to change in the end-stage renal disease population. Additional items evaluating PRO including sexual function, time to recovery after dialysis and patients' self-perceived burden to caregiver were also assessed. All questionnaires were administered by trained interviewers using computer-assisted telephone interviewing to ensure blinding and minimizing selection bias. Interim analysis reveals that these instruments can be used to collect a comprehensive set of HRQOL measures with minimal patient burden. CONCLUSIONS: Accurate measurement of HRQOL and PRO can help us test whether hemodialysis interventions improve the health and well-being of this compromised patient population. We have shown that a comprehensive set of HRQOL measures can be centrally collected through telephone interviews in a blinded fashion, in a way that is well tolerated with minimum respondent burden.
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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 | 0.288 | 0.311 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".