Assessing the Early Impact of the COVID-19 Pandemic on Spine Surgery Fellowship Education
Bibliographic record
Abstract
STUDY DESIGN: This was a cross-sectional study. OBJECTIVE: The objective of this study is to report the impact of COVID-19 on spine surgery fellow education and readiness for practice. SUMMARY OF BACKGROUND DATA: COVID-19 has emerged as one of the most devastating global health crises of our time. To minimize transmission risk and to ensure availability of health resources, many hospitals have cancelled elective surgeries. There may be unintended consequences of this decision on the education and preparedness of current surgical trainees. MATERIALS AND METHODS: A multidimensional survey was created and distributed to all current AO Spine fellows and fellowship directors across the United States and Canada. RESULTS: Forty-five spine surgery fellows and 25 fellowship directors completed the survey. 62.2% of fellows reported >50% decrease in overall case volume since cancellation of elective surgeries. Mean hours worked per week decreased by 56.2%. Fellows reported completing a mean of 188.4±64.8 cases before the COVID-19 crisis and 84.1% expect at least an 11%-25% reduction in case volume compared with previous spine fellows. In all, 95.5% of fellows did not expect COVID-19 to impact their ability to complete fellowship. Only 2 directors were concerned about their fellows successfully completing fellowship; however, 32% of directors reported hearing concerns regarding preparedness from their fellows and 25% of fellows were concerned about job opportunities. CONCLUSIONS: COVID-19 has universally impacted work hours and case volume for spine surgery fellows set to complete fellowship in the middle of 2020. Nevertheless, spine surgery fellows generally feel ready to enter practice and are supported by the confidence of their fellowship directors. The survey highlights a number of opportunities for improvement and innovation in the future training of spine surgeons. LEVEL OF EVIDENCE: Level III.
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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 | 0.006 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".