Trends and outcomes for non-elective neurosurgical procedures in Central Europe during the COVID-19 pandemic
Bibliographic record
Abstract
The world currently faces the novel severe acute respiratory syndrome coronavirus 2 pandemic. Little is known about the effects of a pandemic on non-elective neurosurgical practices, which have continued under modified conditions to reduce the spread of COVID-19. This knowledge might be critical for the ongoing second coronavirus wave and potential restrictions on health care. We aimed to determine the incidence and 30-day mortality rate of various non-elective neurosurgical procedures during the COVID-19 pandemic. A retrospective, multi-centre observational cohort study among neurosurgical centres within Austria, the Czech Republic, and Switzerland was performed. Incidence of neurosurgical emergencies and related 30-day mortality rates were determined for a period reflecting the peak pandemic of the first wave in all participating countries (i.e. March 16th-April 15th, 2020), and compared to the same period in prior years (2017, 2018, and 2019). A total of 4,752 emergency neurosurgical cases were reviewed over a 4-year period. In 2020, during the COVID-19 pandemic, there was a general decline in the incidence of non-elective neurosurgical cases, which was driven by a reduced number of traumatic brain injuries, spine conditions, and chronic subdural hematomas. Thirty-day mortality did not significantly increase overall or for any of the conditions examined during the peak of the pandemic. The neurosurgical community in these three European countries observed a decrease in the incidence of some neurosurgical emergencies with 30-day mortality rates comparable to previous years (2017-2019). Lower incidence of neurosurgical cases is likely related to restrictions placed on mobility within countries, but may also involve delayed patient presentation.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".