Estimating Preference Scores in Conventional and Home Nocturnal Hemodialysis Patients
Bibliographic record
Abstract
Previous studies have reported higher quality of life in patients who receive home nocturnal hemodialysis (HNHD) than conventional in-center hemodialysis (IHD). The optimal method for eliciting preferences from dialysis patients remains undefined, and there may be unique methodologic concerns in this population. Patients' preferences for IHD (n = 20) and HNHD (n = 24) were studied using the standard gamble (SG), time trade-off (TTO), and modified willingness to pay (WTP) methods. This report describes experience with operationalizing these three techniques in this population. A higher preference for HNHD was found with all measures, with significant differences observed with the SG (HNHD: median 0.79 [interquartile range (IQR) 0.67 to 0.95]; IHD: median 0.60 [IQR 0.20 to 0.82]; P = 0.031) and WTP (HNHD: median 0.50 [IQR 0.40 to 0.68]; IHD: median 0.20 [IQR 0.20 to 0.38]; P < 0.001). SG and TTO scores were moderately correlated but not with WTP. In addition, qualitative issues arose during TTO and WTP interviews that seemed to influence the interpretation of these preference scores. In the TTO, time willing to trade became oriented toward the next pivotal life event, with a failure of the requirement for a constant proportional time trade-off. WTP preferences were oriented toward the smallest survival stipend. These issues represent range restriction biases. No significant issues arose during the SG interviews. HNHD patients expressed a greater preference for current health than IHD patients. The operational performance of SG was good in this study, whereas biases and methodologic concerns were identified with the TTO and WTP in this population.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".