Building the Evidence in Peritoneal Dialysis: Use of Randomized Controlled Trials, and Observational and Registry Data
Bibliographic record
Abstract
Renal replacement therapy (RRT) has achieved widespread acceptance without being subjected to the rigors of randomized controlled clinical trials (RCCTs). The RCCT remains the "gold standard" of evidenced-based medicine, but ethical, logistic, and financial limitations mean that not all questions are amenable to a RCCT. Renal registries collect, aggregate, analyze, and interpret data on the occurrence and outcome of renal failure in a defined population. Observational data can be used only to show associations, not causality. Nevertheless, most clinical practice guidelines in nephrology are derived from observational data. The nephrology community needs to join forces to decide the questions that deserve the time, energy, and resources of an RCCT. Prospective observational data can be enhanced by collaboration, standardized definitions, development of a risk-adjustment tool, and consensus among the key players, including professional associations, government, industry, and hospitals. The challenge is to provide evidence-based practice guidelines for the delivery of care to the end-stage renal patient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.683 | 0.886 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.009 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".