Broad-Based Fellowships: A Cornerstone of Minimally Invasive Surgery Education and Dissemination
Bibliographic record
Abstract
Aware of the trends in surgery and of public demand, many residents completing a 5-year training program seek fellowships in minimally invasive surgery (MIS) because of inadequate exposure to advanced MIS during their residency. A survey was designed to evaluate the effectiveness of a broad-based fellowship in advanced laparoscopic surgery offered in an academic health science center. The questionnaire was mailed to all graduates. Data on demographics, comfort level with specific laparoscopic procedures, and opinions regarding the best methods of acquiring these skills were collected. Most of the surgeons entered the fellowship directly after residency. The majority of these surgeons are academic surgeons. Fellows performed a median of 187 cases by the end of their training and felt comfortable operating on foregut, hindgut, and end organ. A full year of training was found to be the best format for appropriate skill transfer. A broad-based MIS fellowship meets the needs of both academic and community surgeons desiring to perform advanced laparoscopic procedures.
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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 | 0.046 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".