Kidney Beam-A Cost-Effective Digital Intervention to Improve Mental Health
Bibliographic record
Abstract
Background There is inequity in provision of physical rehabilitation services for people living with chronic kidney disease (CKD). The Kidney BEAM trial evaluated the clinical value and cost effectiveness of a physical activity digital health intervention in CKD. Methods In a single-blind, 11 centre, randomised controlled trial, 340 adult participants with CKD were randomly assigned to either the Kidney BEAM physical activity digital health intervention or a waitlist control. This study assesses the difference in the Kidney Disease Quality of Life Short Form 1.3 Mental Component Summary (KDQoL-SF1.3 MCS) between intervention and control groups at 6 months, and cost-effectiveness of the intervention. Results At 6 months there was a significant difference in mean adjusted change in KDQoL MCS score between Kidney BEAM and waitlist control (intention-to-treat adjusted mean: 5.9 {95% confidence interval: 4.4 to 7.5} arbitrary units, p<0.0001), and a 93% and 98% chance of the intervention being cost-effective at a willingness to pay threshold of £20,000 and £30,000 per quality-adjusted life year gained. Conclusion The Kidney BEAM physical activity digital health intervention is a clinically valuable and cost-effective means to improve mental health related quality of life in people with CKD (trial registration no. NCT04872933).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.016 | 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".