Academic productivity in pediatric neurosurgery in relation to elective surgery slowdown during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVE: COVID-19 has not only impacted healthcare systems directly via hospitalizations and resource utilization, but also indirectly via adaptations in healthcare practice, such as the evolution of the academic environment and the rise of telemedicine and virtual education. This void in clinical responsibilities has been filled with academic productivity in various fields. In this study the authors investigate the influence of COVID-19 on the academic focus within pediatric neurosurgery. METHODS: All data were obtained from the Journal of Neurosurgery: Pediatrics (JNS Peds). The number of submissions for each month from January 2017 to December 2021 was collected. Data including number of publications, publication level of evidence (LOE), and COVID-19-related articles were collected and verified. Each publication was categorized by manuscript and LOE according to adaptations from the Canadian Task Force on Periodic Health Examination. Publication groups were categorized as pre-COVID-19 (January 2017-February 2020), peri-COVID-19 (March 2020-July 2020), and post-COVID-19 (August 2020-December 2021). Statistical analysis was performed to compare pre-COVID-19, peri-COVID-19, and post-COVID-19 academic volume and quality. RESULTS: During the study time period, a total of 3116 submissions and 997 publications were identified for JNS Peds. Only 2 articles specifically related to COVID-19 and its impact on pediatric neurosurgery were identified, both published in 2021. When analyzing submission volume, a statistically significant increase was seen during the shutdown relative to pre-COVID-19 and post-shutdown time periods, and a significant decrease was seen post-shutdown relative to pre-COVID-19. LOE changed significantly as well. When comparing pre-COVID-19 versus post-COVID-19 articles, a statistically significant increase was identified only in level 4 publications. When analyzing pre-COVID-19 versus post-COVID-19 (2020) and post-COVID-19 (2021), a statistically significant decrease in level 3 and increases in levels 4 and 5 were identified during post-COVID-19 (2020), with a rebound increase in level 3 and a decrease in level 5 during post-COVID-19 (2021). CONCLUSIONS: There was a significant increase in manuscript submission during the initial pandemic period. However, there was no change during subsequent spikes in COVID-19-related hospitalizations. Coincident with the initial surge in academic productivity, despite steady publication volume, was an inverse decline in quality as assessed by LOE.
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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 | 0.028 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".