Training in Hepatopancreatobiliary Surgery
Bibliographic record
Abstract
OBJECTIVE: Evaluate the current status of Hepatopancreatobiliary (HPB) Surgery workforce in North America. BACKGROUND: HPB fellowships have proliferated, with HPB surgeons entering the field through 3 pathways: transplant surgery, surgical oncology, or HPB surgery training. Impact of this growth is unknown. METHODS: An anonymous survey was distributed to 654 is used as HPB surgeons from October 2012 to January 2013. Questions evaluated satisfaction with job availability after training and description of current practice. Nationwide Inpatient Sample (NIS) data from 2003 to 2010 was queried to describe the growth of HPB cases in the United States; these data were compared to prior HPB workforce projections performed using 2003 NIS data. RESULTS: A total of 416 HPB surgeons responded (66%). HPB surgeons are concentrated in a small number of states/provinces with a lack of HPB surgeon workforce in central United States. HPB graduates from 2008 to 2012 report increased difficulty in identifying an HPB-focused practice versus prior to 2008. Mature HPB surgery practices report a composition of 25% to 50% non-HPB operative cases. Fifty-one percent of respondents reported an opinion that current HPB Surgeon production was excessive; however, 2010 NIS data demonstrate that major HPB surgery cases have grown significantly more than was previously projected using 2003 NIS data. CONCLUSIONS AND RELEVANCE: A cohesive strategy for responsibly responding to the HPB surgical workforce requirements of North America is needed. Elevation of training standards, standardization of requirements for certification, and careful modeling that accounts for regionalization of care should be pursued to prevent overtraining and decentralization of HPB surgical care in the future.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".