Recruitment of patients with chronic kidney disease and obstructive sleep apnoea for a clinical trial
Bibliographic record
Abstract
Obstructive sleep apnoea is common in chronic kidney disease (CKD) and may accelerate the decline in kidney function. Recruitment for a randomised controlled trial to address whether treatment of sleep apnoea with continuous positive airway pressure (CPAP) slows the progression of kidney failure may be challenging because sleep apnoea is often asymptomatic in this patient population. The present report outlines recruitment challenges and how to address them. Adult patients with CKD were recruited for a 12-month randomised, controlled, non-blinded, parallel clinical trial to evaluate the impact of CPAP therapy on kidney function. Patients completed a home sleep apnoea test and those that met pre-specified sleep apnoea and nocturnal hypoxaemia severity criteria were randomised to receive CPAP or no therapy. Although 1,665 patients were eligible to participate in the study over 3 years, only 57 (3.4%) were ultimately randomised. The sequential reasons (and number of patients) for recruitment failure were: no show at clinic appointment (137), insufficient recruiters to approach every eligible patient (461), on therapy for sleep apnoea (122), unable to provide informed consent (67), refused consent (645), home sleep apnoea test not completed (47) or inclusion criteria not met (116), and declined pre-randomisation education session (12). Many challenges limit effective recruitment, which may be addressed by hiring additional recruiters and increasing the awareness of sleep apnoea among patients with CKD. These findings can be used to improve recruitment strategies and the design of future studies.
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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 | 0.040 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".