Comparative studies of dialysis therapies should reflect real world decision-making
Bibliographic record
Abstract
The incidence and prevalence of end-stage renal disease (ESRD) continues to rise. While transplantation is the preferred therapy for kidney failure, there is a shortage of donor organs, and the majority of patients will be treated with either peritoneal dialysis (PD) or hemodialysis (HD). Randomized controlled trials comparing patient outcomes on PD and HD are not likely to be successful, as individuals who are educated about their treatment options generally develop a strong preference for one therapy over the other and will not consent to randomization. As a result, prospective cohort studies are frequently the strongest study design available to compare outcomes between dialysis modalities. Previous studies have provided important insights into the relative merits of the 2 therapies. However, they have examined outcomes in relatively heterogeneous groups of ESRD patients and are generally not designed in a manner that mirrors clinical decision-making. We explore several key methodological challenges in the design of observational research in ESRD with a focus on minimizing selection bias and making studies more relevant to the practicing nephrologist. We emphasize that incident patients are preferred in most comparative studies of dialysis modalities. We argue that analyses comparing the outcomes of renal replacement therapy (RRT) modalities should include patients eligible for the therapies being compared and that the way that patients are assigned to treatment groups should reflect decision-making in clinical practice. Finally, the point at which baseline characteristics are measured and we begin tracking patients for the occurrence of outcomes should be chosen carefully.
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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 | 0.374 | 0.680 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".