Impact of Suspensions and Reactivations from Waitlist on Quality of Life in Canadian-Australasian Randomised Trial of Screening Kidney Transplant Candidates for Coronary Artery Disease (CARSK)
Bibliographic record
Abstract
Background: There is limited information on individuals’ health-related quality of life (HRQoL) on the waiting list for a kidney transplant. Understanding the HRQoL among patients undergoing suspensions and reactivations could shed light on their experiences and the impact of illness burden. Methods: The HRQoL among eligible participants (not transplanted or deceased within 12 months) was assessed using the EuroQoL 5 domains, five levels (EQ-5D-5L) questionnaire administered at baseline, 6 and 12-months. The HRQoL index ranged from 0 (dead) to 1 (full health). Within these 6-month intervals, the effect of waitlist suspensions and reactivations on HRQoL was examined using Generalized Estimating Equations models. Factors associated with HRQoL such as age, sex, ethnicity, dialysis modality, time on dialysis, diabetes status, cause of end-stage kidney disease, prior kidney transplant, baseline EQ-5D score, and waitlist status (never suspended, suspended but not reactivated, suspended and reactivated) were investigated. Results: 1,457 patients from Australia (32%), New Zealand (17%), and Canada (51%) were recruited. From baseline to 12 months, 1,013 patients were suspended, 170 patients were suspended and not reactivated, and 187 were suspended and reactivated. Average age was 54 years (SD 12), 63% males, 35% diabetics. Of these, 671 (46%) were managed with facility-based hemodialysis, 247 (17%) with home hemodialysis, and 539 (37%) with peritoneal dialysis. On average, patients spent 1,039 days on dialysis. The mean EQ5D index at baseline, 6 months, and 12 months was 0.87 (SD 0.14), 0.89 (SD 0.13), and 0.89 (SD 0.14), respectively. Compared with patients not suspended, those who were suspended and not reactivated had a lower mean EQ5D index (0.042, 95% CI 0.014 to 0.070, p = 0.004). Conclusions: Our findings indicate that patients’ suspension from the waitlist due to health factors making them unfit for a deceased donor kidney transplant significantly impacts their self-reported HRQoL. - Adjusted Mean EQ5D index Visits Never suspended Suspended and not reactivated Suspended and reactivated 6 months 0.891 (95% CI 0.842 - 0.940) 0.894 (95% CI 0.798 - 0.900) 0.878 (95% CI 0.829 - 0.928) 12 months 0.887 (95% CI 0.838 - 0.935) 0.845 (95% CI 0.794 - 0.895) 0.874 (95% CI 0.825 - 0.923)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".