Impact of the COVID-19 Pandemic on the Ability of Surgical Residents to Successfully Complete Residency Caseload Requirements in Private Practice
Bibliographic record
Abstract
A retrospective analysis of the surgical caseloads from five corporate-owned private specialty practices with American College of Veterinary Surgery (ACVS)-registered surgical residency programs was performed. The impact of the COVID-19 pandemic on the ability for private practice ACVS residency programs to provide adequate case numbers to meet residency requirements was evaluated. The surgical caseload was divided into 6-month intervals beginning in September 2019 and ending August 2022. The overall, specific ACVS case log categories and emergency caseloads were compared. Cases were categorized using ACVS training standard definitions. An average of 12 ACVS residents enrolled across the five training programs, with 24,331 cases operated across all hospitals during the 3-year study period. There was no significant increase or decrease in average surgical caseload at any time interval compared with the pre-COVID-19 period. Surgical residents did experience an increased emergency caseload for a portion of the pandemic. However, the increased emergency demands on surgical residents during the COVID-19 pandemic appears to have resolved with time. There was a decrease in neurologic caseload that was seen at four of the five hospitals. Neurosurgical caseload may be more variable among surgeons and this study did not account for overall neurosurgical caseload available to residents. All other categories were adequate or remained consistent throughout the study period, with no evidence of impact from the COVID-19 pandemic. The COVID-19 pandemic also did not negatively impact resident surgical caseload with continued case volumes with exposure to all necessary procedures for resident requirements.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".