Impact of COVID-19 pandemic on the neurosurgical practice in Egypt
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic and the subsequent lockdown have significantly altered many aspects of the health care services. We investigated the impact of the restrictive measures during the pandemic on the volume and spectrum of operated neurosurgical cases at two University hospitals in Egypt. Results The total number of surgeries dropped during the lockdown (second quarter of the year 2020) by 38%, compared with the total number of surgeries in the first quarter of the same year, with an increase in the proportion of urgent surgeries to the total number of surgeries from 46 to 69% (P < 0.001), and a decrease in the proportion of elective surgeries from the total number of neurosurgeries from 54 to 31% (P < 0.001). Similar differences were noted in the volume and spectrum of surgeries in the second quarter of 2020, when compared to the same period of the preceding year (2019). Conclusions The COVID-19 pandemic has significantly altered the nature and volume of neurosurgical practice. The overall number of surgeries showed a marked decline in the lockdown period; however, the numbers of urgent surgeries showed no significant difference under the lockdown.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".