Does experience matter? The relationship between nephrologist characteristics and end stage kidney disease patient outcomes
Bibliographic record
Abstract
BACKGROUND: Nephrology offers the unique opportunity to directly link patients to providers, allowing the study of patient outcomes at the provider level. The purpose of this analysis was to determine whether nephrologist experience, defined as years in nephrology practice, was associated with clinical outcomes. DESIGN: Physician data contained within the American Medical Association (AMA) Physician Masterfile was combined with patient and Medicare claims data from the United States Renal Data System (USRDS) for the calendar year 2012, with follow up extending through June 30, 2014. Associations with important healthcare outcomes including mortality in patients receiving maintenance renal replacement therapy (RRT), waitlisting for kidney transplantation, and receipt of a kidney transplant were determined with broad adjustment for both patient and provider level variables, with attention on tertile of provider time in practice. RESULTS: We identified 256,324 patients on maintenance RRT cared for by 6193 nephrologists. Nephrologists with the least experience were more likely to be female, reside in a region with ≥1,000,000 people, have a Doctor of Osteopathic Medicine degree, and have a listed maintenance of certification status as "yes." Overall, 30.2% of the cohort died at a mean follow up of 1.99 years. Compared to those with the 0-10 years of experience, receipt of care from nephrologists with more experience was associated with lower mortality (AHR 0.97 CI 0.94-0.99 for nephrologists with 11-20 years) and increased listing for kidney transplantation (AHR 1.10; CI 1.01-1.21 for nephrologists with >21 years experience). Experience level did not result in a difference in kidney transplantation rates. CONCLUSIONS: Receipt of maintenance RRT from nephrologists with greater experience was associated with decreased mortality and increased listing for kidney transplantation, an effect that remained significant after multiple adjustments for important patient and nephrologist variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".