Nephrologist Caseload and Hemodialysis Patient Survival in an Urban Cohort
Bibliographic record
Abstract
Physician caseload may be a predictor of patient outcomes associated with various medical conditions and procedures, but the association between patient-physician ratio and mortality among patients undergoing hemodialysis has not been determined. We examined whether a higher patient-nephrologist ratio affects patient mortality risk using de-identified data from DaVita dialysis clinics and the U.S. Renal Data System. A total of 41 nephrologists with a caseload of 50-200 hemodialysis patients from an urban California region were retrospectively ranked according to their hemodialysis patient mortality rate during a 6-year period between 2001 and 2007. We calculated all-cause mortality hazard ratios for each nephrologist and compared patient- and provider-level characteristics between the 10 nephrologists with the highest patient mortality rates and the 10 nephrologists with the lowest patient mortality rates. Nephrologists with the lowest patient mortality rates had significantly lower patient caseloads than nephrologists with the highest mortality rates (median [interquartile range], 65 [55-76] versus 103 [78-144] patients per nephrologist, respectively; P<0.001). Additionally, patients treated by nephrologists with the lowest patient mortality rates received higher dialysis doses, had longer sessions, and received more kidney transplants. In demographic characteristic-adjusted analyses, each 50-patient increase in caseload was associated with a 2% increase in patient mortality risk (hazard ratio, 1.02; 95% confidence interval, 1.00 to 1.04; P<0.001). Hence, these results suggest that nephrologist caseload influences hemodialysis patient outcomes, and future research should focus on identifying the factors underlying this association.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".