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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".