Impact of Modality Choice on Rates of Hospitalization in Patients Eligible for Both Peritoneal Dialysis and Hemodialysis
Bibliographic record
Abstract
BACKGROUND: Hospitalization rates are a relevant consideration when choosing or recommending a dialysis modality. Previous comparisons of peritoneal dialysis (PD) and hemodialysis (HD) have not been restricted to individuals who were eligible for both therapies. ♢ METHODS: We conducted a multicenter prospective cohort study of people 18 years of age and older who were eligible for both PD and HD, and who started outpatient dialysis between 2007 and 2010 in four Canadian dialysis programs. Zero-inflated negative binomial models, adjusted for baseline patient characteristics, were used to examine the association between modality choice and rates of hospitalization. ♢ RESULTS: The study enrolled 314 patients. A trend in the HD group toward higher rates of hospitalization, observed in the primary analysis, became significant when modality was treated as a time-varying exposure or when the population was restricted to elective outpatient starts in patients with at least 4 months of pre-dialysis care. Cardiovascular disease, infectious complications, and elective surgery were the most common reasons for hospital admission; only 23% of hospital stays were directly related to complications of dialysis or kidney disease. ♢ CONCLUSIONS: Efforts to promote PD utilization are unlikely to result in increased rates of hospitalization, and efforts to reduce hospital admissions should focus on potentially avoidable causes of cardiovascular disease and infectious complications.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".