Reporting of key methodological and ethical aspects of cluster trials in hemodialysis require improvement: a systematic review
Bibliographic record
Abstract
Abstract Background The hemodialysis setting is suitable for trials that use cluster randomization, where intact groups of individuals are randomized. However, cluster randomized trials (CRTs) are complicated in their design, analysis, and reporting and can pose ethical challenges. We reviewed CRTs in the hemodialysis setting with respect to reporting of key methodological and ethical issues. Methods We conducted a systematic review of CRTs in the hemodialysis setting, published in English, between 2000 and 2019, and indexed in MEDLINE or Embase. Two reviewers extracted data, and study results were summarized using descriptive statistics. Results We identified 26 completed CRTs and five study protocols of CRTs. These studies randomized hemodialysis centers (n = 17, 55%), hemodialysis shifts (n = 12, 39%), healthcare providers (n = 1, 3%), and nephrology units (n = 1, 3%). Trials included a median of 28 clusters with a median cluster size of 20 patients. Justification for using a clustered design was provided by 15 trials (48%). Methods that accounted for clustering were used during sample size calculation in 14 (45%), during analyses in 22 (71%), and during both sample size calculation and analyses in 13 trials (42%). Among all CRTs, 26 (84%) reported receiving research ethics committee approval; patient consent was reported in 22 trials: 10 (32%) reported the method of consent for trial participation and 12 (39%) reported no details about how consent was obtained or its purpose. Four trials (13%) reported receiving waivers of consent, and the remaining 5 (16%) provided no or unclear information about the consent process. Conclusion There is an opportunity to improve the conduct and reporting of essential methodological and ethical issues in future CRTs in hemodialysis. Review Registration We conducted this systematic review using a pre-specified protocol that was not registered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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 teacher head, 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".