An analysis of fellowship training of kidney transplant surgeons in a Brazilian state
Bibliographic record
Abstract
INTRODUCTION: The lack of specialized professionals potentially contributes to the inability to meet the demand for kidney transplantations. Moreover, there is no universal proposal for the training process of transplantation surgeons. We aimed to explore the characteristics of the training program and professional activities of kidney transplantation teams in the state of Minas Gerais, Brazil. METHODS: We invited the surgeons of all 19 active kidney transplantation centers in Minas Gerais to participate in this cross-sectional study. Demographic and professional training data were compared using linear and logistic regression models. RESULTS: The response rate among the centers was high (89%); half of the surgeons answered the survey (39/78). Most of the centers were public teaching institutions, under a production-based payment contract, with a mean of 6 ± 2.4 surgeons/team; 94.2% of the centers had urologists. The surgeons were 95% male (age of 46.3 ± 9.7 years) and 59% were urologists. Most were involved in organ procurement and transplantation; only one surgeon worked exclusively with transplantation. The mean period since training was 13 ± 9.4 years, with a mean of 10 ± 9.7 years as part of the transplantation team. Only 25.6% had specialized or formal training in transplantation, with only one completing a formal medical residency for kidney transplantation. The lack of training programs was the most frequently cited reason. CONCLUSION: Kidney transplantation surgeons are not exclusive and most have not completed a formal fellowship program in transplantation because they are not available. These data indicate the need to improve training programs and facilitate the formation of new kidney transplantation teams.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".