Nationwide Effect of COVID-19 on Cases Performed During Pediatric Orthopaedic Surgery Fellowship Training in the United States
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic negatively affected surgical training in the United States. We hypothesized that reported case volume during pediatric orthopaedic surgery fellowship training would decrease markedly during the 2019 to 2020 academic year, which corresponded with the COVID-19 outbreak. METHODS: The Accreditation Council for Graduate Medical Education provided nationwide case logs for accredited pediatric orthopaedic surgery fellows (2017 to 2021). Annual reported case volumes were extracted and summarized as means ± SD. Parametric tests were used to compare annual case volumes. RESULTS: A total of 149 pediatric orthopaedic fellows from 23 accredited fellowships were included. A 16% year-over-year (YoY) decrease was noted in the reported case volume during the 2019 to 2020 academic year (238 ± 80 vs. 255 ± 60, P < 0.001). Nonacute case categories had the most notable YoY percentage decreases: Soft Tissue: Transfer, Lengthen, Release (-42%); Clubfoot (-34%); and Foot and Ankle Deformity (-31%). Acute case categories had the most notable YoY percentage increases: Trauma Lower Limb (12%) and Trauma Upper Limb (10%). A subsequent 42% YoY increase was noted in the reported case volume during the 2020 to 2021 academic year. DISCUSSION: A 16% YoY decrease was noted in the reported case volume during the 2019 to 2020 academic year, which corresponded to widespread economic shutdowns during the initial COVID-19 outbreak. Nonacute cases experienced the greatest negative effect. The results from this study may inform the orthopaedic surgery community on the effect of future national emergencies, such as viral outbreaks.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".