Variations in Outcomes of Surgical Oncology Fellowship Graduates Performing Hepatopancreatic Surgery in the United States Based on Fellowship Training Program
Bibliographic record
Abstract
BACKGROUND: Exposure in hepatopancreatic (HP) surgery can vary among Complex General Surgical Oncology (CGSO) fellowship graduates. Real-world outcomes of fellowship graduates performing HP surgery have not been previously examined. METHODS: Medicare beneficiaries undergoing HP surgery for cancer between 2016 and 2021 were identified. Surgeon-level data, including fellowship training information, were linked to patient-level Medicare data. Trends and variations in severe complications and 90-day mortality according to fellowship training were examined. RESULTS: Overall, 9954 HP cancer operations (pancreatectomy: 7,566, 76%; hepatectomy: 2,388, 24%) were performed between 2016 and 2021. A total of 609 CGSO fellowship graduates trained at 42 different CGSO programs in the United States or Canada were identified. Most cases (93.2%) were performed by surgeons who had completed an ACGME-accredited CGSO program. Almost half of HP operations were performed by graduates of two specific CGSO programs (n = 4,769, 47.9%), whereas 92.1% (n = 9,166) of HP operations were performed by graduates of 15 CGSO programs. After adjusting for relevant, multilevel characteristics, marked variations in outcomes by CGSO fellowship program were noted following both hepatectomy and pancreatectomy. The adjusted probability of serious complications decreased from 2016 to 2021 (16.4% vs. 12.9%; p < 0.05), however, the likelihood of 90-day mortality remained relatively stable during the study period (2016: 6.4% vs. 2021: 5.3%; p = 0.19). CONCLUSIONS: While outcomes of CGSO graduates improved over time, a marked variation in outcomes of graduates performing HP surgery was noted based on their fellowship training. Further efforts should be made to enhance and standardize HP surgery exposure and training in CGSO programs for fellows intending to perform HP surgery in practice.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".