A look at the global impact of COVID-19 pandemic on neurosurgical services and residency training
Bibliographic record
Abstract
Background: The COVID-19 pandemic has left an indelible effect on healthcare delivery and education system, including residency training. Particularly, neurosurgical departments worldwide had to adapt their operating model to the constantly changing pandemic landscape. This review aimed to quantify the reduction in neurosurgical operative volume and describe the impact of these trends on neurosurgical residency training. Methods: We performed a comprehensive search of PubMed and EMBASE between December 2019 and October 2022 to identify studies comparing pre-pandemic and pandemic neurosurgical caseloads as well as articles detailing the impact of COVID-19 on neurosurgery residency training. Statistical analysis of quantitative data was presented as pooled odds ratio (OR) and 95% confidence intervals (CI). Results: A total of 49 studies met the inclusion criteria, of which 12 (24.5%) were survey-based. The case volume of elective surgeries and non-elective procedures decreased by 70.4% (OR=0.296, 95%CI 0.210-0.418) and 68.2% (OR=0.318, 95%CI 0.193-0.525), respectively. A significant decrease was also observed in functional (OR=0.542, 95%CI 0.394-0.746), spine (OR=0.545, 95%CI 0.409-0.725), and skull base surgery (OR=0.545, 95%CI 0.409-0.725), whereas the caseloads for tumor (OR=1.029, 95%CI 0.838-1.263), trauma (OR=1.021, 95%CI 0.846-1.232), vascular (OR=1.001, 95%CI 0.870-1.152), and pediatric neurosurgery (OR=0.589, 95%CI 0.344-1.010) remained relatively the same between pre-pandemic and pandemic periods. The reduction in caseloads had caused concerns among residents and program directors in regard to the diminished clinical exposure, financial constraints, and mental well-being. Some positives highlighted were rapid adaptation to virtual educational platforms and increasing time for self-learning and research activities. Conclusion: While COVID-19 has brought about significant disruptions in neurosurgical practice and training, this unprecedented challenge has opened the door for technological advances and collaboration that broaden the accessibility of resources and reduce the worldwide gap in neurosurgical education.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".