Current trends and outcomes of non-elective neurosurgical care in Central Europe during the second year of the COVID-19 pandemic
Bibliographic record
Abstract
Reflecting the first wave COVID-19 pandemic in Central Europe (i.e. March 16th-April 15th, 2020) the neurosurgical community witnessed a general diminution in the incidence of emergency neurosurgical cases, which was impelled by a reduced number of traumatic brain injuries (TBI), spine conditions, and chronic subdural hematomas (CSDH). This appeared to be associated with restrictions imposed on mobility within countries but also to possible delayed patient introduction and interdisciplinary medical counseling. In response to one year of COVID-19 experience, also mapping the third wave of COVID-19 in 2021 (i.e. March 16 to April 15, 2021), we aimed to reevaluate the current prevalence and outcomes for emergency non-elective neurosurgical cases in COVID-19-negative patients across Austria and the Czech Republic. The primary analysis was focused on incidence and 30-day mortality in emergency neurosurgical cases compared to four preceding years (2017-2020). A total of 5077 neurosurgical emergency cases were reviewed. The year 2021 compared to the years 2017-2019 was not significantly related to any increased odds of 30 day mortality in Austria or in the Czech Republic. Recently, there was a significant propensity toward increased incidence rates of emergency non-elective neurosurgical cases during the third COVID-19 pandemic wave in Austria, driven by their lower incidence during the first COVID-19 wave in 2020. Selected neurosurgical conditions commonly associated with traumatic etiologies including TBI, and CSDH roughly reverted to similar incidence rates from the previous non-COVID-19 years. Further resisting the major deleterious effects of the continuing COVID-19 pandemic, it is edifying to notice that the neurosurgical community´s demeanor to the recent third pandemic culmination keeps the very high standards of non-elective neurosurgical care alongside with low periprocedural morbidity. This also reflects the current state of health care quality in the Czech Republic and Austria.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".