Operative Mortality After Renal Transplantation—Does Surgeon Type Matter?
Bibliographic record
Abstract
PURPOSE: Currently there are 64 accredited renal transplantation fellowships in Canada and the United States. Only 27% are limited in scope to kidney transplants. In the remaining fellowships the trainee learns to transplant multiple abdominal organs. Given this evolution to the multiorgan transplant surgeon, we evaluated the effect of the current training paradigm on practice patterns and outcomes for kidney transplants. MATERIALS AND METHODS: Using data from the Nationwide Inpatient Sample, discharge records for kidney transplants (6,674) were abstracted (1993 to 2003). Through the Nationwide Inpatient Sample unique surgeon identifier we determined the proportion of kidney transplants performed by multiorgan and kidney only transplant surgeons. We fit multilevel regression models to examine the relationship between surgeon type and transplant outcome. RESULTS: We identified 99 multiorgan and 196 kidney only transplant surgeons who performed 3,255 and 3,419 kidney transplants, respectively. Kidney only transplant surgeons were more likely than multiorgan surgeons to practice in nonteaching, private, for-profit hospitals (p <0.05). Unadjusted operative mortality was higher in patients treated by kidney only vs multiorgan transplant surgeons (1.7% vs 0.9%, p = 0.002). After adjusting for patient and hospital factors, those who underwent renal transplantation performed by multiorgan transplant surgeons had 55% lower odds of inpatient death (OR 0.45, 95% CI 0.26-0.76) vs kidney only transplant surgeons. CONCLUSIONS: Despite the current training paradigm, kidney only transplant surgeons have a prominent role in renal transplantation. However, given the current donor organ shortage and the implications for quality, the observed mortality difference suggests that additional investigation is needed to determine whether this role should be decreased.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| 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 | 0.001 |
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".