Peritoneal dialysis: why not?
Bibliographic record
Abstract
Peritoneal Dialysis (PD) as an option for Renal Replacement Therapy (RRT) for end-stage Chronic Kidney Disease (CKD).It has the advantage of being a home-based, portable modality, and probably due to its continuous character, it preserves residual renal function (RRF) for longer 1 .Despite these aspects, its use is still low in Brazil.According to the 2021 Brazilian Dialysis Census, we have only 5.8% of the population on chronic dialysis submitted to PD 2 .Could this low prevalence be explained by unfavorable outcomes associated with this modality?This is not what the literature shows.Vicentini and Ponce's study 3 , published in this issue, compared outcomes in a cohort of incident patients on planned and urgent-onset PD and HD over a 5-year period.The authors found no difference in survival between the modalities, demonstrating the non-inferiority of PD in relation to HD in a Brazilian center.This finding is corroborated by other publications.In an analysis comparing incident dialysis patients in Canada eligible for both HD and PD, Wong et al. found no difference in mortality between both methods 4 .In a systematic review using propensity scores, which are commonly used in individuals from different treatment groups to achieve balance in the distribution of confounding factors, allowing direct estimation of causal effects of treatment, Elsayed et al showed that PD and HD provided equivalent survival benefits, and that reported differences in outcomes between treatments largely reflect a combination of factors that are unrelated to clinical efficacy 5 .
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".