Survival comparisons between haemodialysis and peritoneal dialysis
Bibliographic record
Abstract
Dialysis modality selection for end-stage renal disease patients should not solely be dictated by survival comparisons but also take into account patient preference. Nevertheless, potential mortality differences between dialysis modalities in (subgroups of) patients may contribute to modality choice. Survival comparisons have therefore frequently been made. With one exception, the investigators used an observational study design to study this issue. Observational studies fulfil a valuable role in nephrology research, but their most important drawback is that selection bias by the clinician may occur [1]. Even after adjustment for potential confounders in the statistical analysis, there is usually at least some amount of residual confounding due to unmeasured variables. This may prevent a fair comparison of outcomes between patient groups, something that is usually feasible from well-conducted randomized controlled trials. Almost a decade ago, such a trial with random allocation of dialysis modality was unsuccessful because patient and physician preference turned out to play a crucial role in modality choice [2]. Despite this unsuccessful attempt, a new trial has started in China (trial registration NCT01413074 at clinicaltrials.gov). However, until the results of this trial are presented, mortality in haemodialysis and peritoneal dialysis patients can only be compared based on large-scale observational studies.
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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".