Increasing early career surgeon engagement in the Society for Vascular Surgery: A report of the Society’s Young Surgeons Section Steering Committee
Bibliographic record
Abstract
When examining the US surgical workforce, a shortage of approximately 30,000 surgeons is predicted by 2030. This shortage is attributed to the increasing surgical needs of the nation's aging population and the increased rate of retirement in the surgeon workforce. As such, the surgeon workforce will rely on Millennials and Generation Z to grow and expand their role in healthcare. To address these changes, the Society for Vascular Surgery (SVS) established the Young Surgeons Section (YSS) in 2022 after formal approval by the Society's Executive Board. The YSS Steering Committee set forth in 2022 with an initial charge focused on identifying the needs of this demographic, beginning to develop educational content focused on early career surgeons, providing and advocating for leadership opportunities within the SVS, and assisting the Society in its membership recruitment efforts. The goal of this report is to provide the context under which the YSS was started, outline the major accomplishments of the Section over its first 2 years, and to begin to discuss the needed next steps for the SVS to ensure continued engagement.
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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 | 0.078 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".