Lack of Association between Dialysis Modality and Outcomes in Atheroembolic Renal Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Atheroembolic renal disease (AERD) can require dialytic support. Because anticoagulation may trigger atheroembolization, peritoneal dialysis may be preferred to hemodialysis. However, the effect of dialysis modality on renal and patient outcomes in AERD is unknown. DESIGN, SETTINGS, PARTICIPANTS, & MEASUREMENTS: A subcohort of 111 subjects who developed acute/subacute renal failure requiring dialysis was identified from a larger longitudinal study of AERD. The main exposure of interest was dialysis modality (peritoneal versus extracorporeal therapies). Logistic regression was used to study the probability of renal function recovery. Times from dialysis initiation to death were studied using Cox's regression. RESULTS: Eighty-six patients received hemodialysis and 25 received peritoneal dialysis. The probability of renal function recovery was similar by dialysis modality (25% among hemodialysis patients and 24% among peritoneal dialysis patients; P = 0.873). During follow-up, 58 patients died, 14 among peritoneal patients and 44 among hemodialysis patients (P = 0.705). In multivariable analysis, gastrointestinal tract involvement and use of statins maintained an independent effect on the risk of patient death. CONCLUSIONS: This study does not support the notion that one dialysis modality is superior to the other. However, the observational nature of the data precludes any firm conclusions.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".