Dialysis modality and survival: Done to death
Bibliographic record
Abstract
The debate surrounding whether peritoneal dialysis or hemodialysis is associated with differential survival continues as the numerous comparative studies over the past 3 decades have yielded conflicting results. Findings have also evolved over time in the setting of changing patient characteristics, advances in dialytic technologies, and the use of more robust statistical and epidemiologic approaches. Here, we will critically review the body of evidence, both historical and contemporary, comparing survival across dialysis modalities. Significant limitations of the observational nature of the current literature will be highlighted given that no adequately powered randomized controlled trials exist. Given the lack of consistency and limitations of current studies, coupled with the poor survival across both modalities, we can likely conclude that survival comparisons between both modalities do not appreciably differ. Hence, the choice of dialysis modality should not be dictated by survival comparisons, but rather be based on an individualized and informed decision making that places patient preference and lifestyle considerations at the forefront, while integrating medical factors and availability of resources and support. The emphasis of future research should move beyond survival outcomes when comparing dialysis modalities, and instead be redirected to patient-endorsed and patient-reported outcomes.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".