Effect of Spine Fellow Training on Operative Outcomes, Affirming Graduated Responsibility
Bibliographic record
Abstract
STUDY DESIGN: Retrospective review of prospectively collected surgical data. OBJECTIVE: This study sought to determine the effect of fellow education during the course of the academic year (August-July) on surgical outcomes in adolescent idiopathic scoliosis. One surgeon and one type of surgery were chosen to minimize confounding factors. SUMMARY OF BACKGROUND DATA: Educating and training the next generation of physicians and surgeons is necessary for the survival and continuation of medical care. There has been recent momentum to document scientifically that medical education is safe. Spine surgery is complex and demanding, with a steep learning curve, making it an ideal model to detect any potential negative impact of medical education. SUBJECTS: adolescent patients undergoing posterior spinal surgery, between August 2007 and July 2010, by a single senior surgeon at one institution with a fellow as the only surgical assistant. Demographic and perioperative data were collected and then segmented by surgical date into quarters according to the rotations of the academic year. One fellow was included in each quarter during the 4 years, resulting in 16 fellows across the 4 quarters. An analysis of variance model was used to assess differences in operative time, blood loss, length of stay, and complications between the quarters of the year. RESULTS: There were no significant differences between the groups regarding age, sex, or Lenke curve type. No statistically significant differences were found between the 4 quarters of the fellowship year for estimated blood loss, use of cell saver, length of stay, operative time, and complication rate. CONCLUSION: This study is the first to show that fellow education during the course of the academic year did not impact the patient outcomes studied. It is clear that while there is significant academic benefit for the fellows as they complete their spine fellowship, there is no negative impact for patients. LEVEL OF EVIDENCE: 4.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".