Transplant Surgery Fellow Perceptions About Training and the Ensuing Job Market—Are the Right Number of Surgeons Being Trained?
Bibliographic record
Abstract
The American Society of Transplant Surgeons (ASTS) sought whether the right number of abdominal organ transplant surgeons are being trained in the United States. Data regarding fellowship training and the ensuing job market were obtained by surveying program directors and fellowship graduates from 2003 to 2005. Sixty-four ASTS-approved programs were surveyed, representing 139 fellowship positions in kidney, pancreas and/or liver transplantation. One-quarter of programs did not fill their positions. Forty-five fellows graduated annually. Most were male (86%), aged 31-35 years (57%), married (75%) and parents (62%). Upon graduation, 12% did not find transplant jobs (including 8% of Americans/Canadians), 14% did not get jobs for transplanting their preferred organ(s), 11% wished they focused more on transplantation and 27% changed jobs early. Half fellows were international medical graduates; 45% found US/Canadian transplant jobs, particularly 73% with US/Canadian residency training. Fellows reported adequate exposure to training volume, candidate selection, pre/postoperative care and organ procurement, but not to donor management/selection, outpatient care and core didactics. One-sixth noted insufficient 'mentoring/preparation for a transplantation career'. Currently, there seem to be enough trainees to fill entry-level positions. One-third program directors believe that there are too many trainees, given the current and foreseeable job market. ASTS is assessing the total workforce of transplant surgeons and evolving manpower needs.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".