Transplant Surgery Pipeline: A Report from the American Society of Transplant Surgeons Pipeline Taskforce
Bibliographic record
Abstract
BACKGROUND: Transplant surgery fellowship has evolved over the years and today there are 66 accredited training programs in the US and Canada. There is growing concern, however, about the number of US-trained general surgery residents pursuing transplant surgery. In this study, we examined the transplant surgery pipeline, comparing it with other surgical subspecialty fellowships, and characterized the resident transplantation experience. METHODS: Datasets were compiled and analyzed from surgical fellowship match data obtained from the National Resident Matching Program and ACGME reports and relative fellowship competitiveness was assessed. The surgical resident training experience in transplantation was evaluated. RESULTS: From 2006 to 2018, a total of 1,094 applicants have applied for 946 transplant surgery fellowship positions; 299 (27.3%) were US graduates. During this period, there was a 0.8% decrease per year in US-trained surgical residents matching into transplant surgery (p = 0.042). In addition, transplant surgery was one of the least competitive fellowships compared with other National Resident Matching Program surgical subspeciality fellowships, as measured by the number of US applicants per available fellowship position, average number of fellowship programs listed on each applicant's rank list, and proportion of unfilled fellowship positions (each, p < 0.05). Finally, from 2015 to 2017, there were 57 general surgery residency programs that produced 77 transplant surgery fellows, but nearly one-half of the fellows (n = 36 [46.8%]) came from 16 (28.1%) programs. CONCLUSIONS: Transplant surgery is one of the least competitive and sought after surgical fellowships for US-trained residents. These findings highlight the need for dedicated efforts to increase exposure, mentorship, and interest in transplantation to recruit strong US graduates.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".