The management and outcomes of coronavirus disease 2019 infection in a series of neurosurgical patients
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has impacted neurosurgical practice worldwide. In Iran, hospitals have halted their routine activities, and most hospital beds have been assigned to COVID-19 patients. Here, we share our experience with 10 neurosurgical cases with confirmed COVID-19. MATERIALS AND METHODS: From February 24, 2020 to April 20, 2020, we were able to obtain clinical data on ten neurosurgical patients with COVID-19 through a predefined electronic form. RESULTS: Of the 10 patients with COVID-19 on neurosurgical units, eight underwent surgical interventions. The age of the patients ranged from 21 to 75 years and 70% were males. The diagnosis of COVID-19 was based on chest imaging findings and reverse transcriptase-polymerase chain reaction for coronavirus and an infectious disease specialist and a pulmonologist confirmed the diagnoses. In two cases, there was a significant decrease in O2 saturation intraoperatively. Three patients in this series died during the assessment period. One death was due to respiratory failure induced by the coronavirus infection. The cause of death in other two patients was cardiovascular failure not related to COVID-19. CONCLUSIONS: We hope we can provide a reference for future studies and help develop a clearer understanding of neurosurgical practice and outcomes in patients with COVID-19. In the time of COVID-19 pandemic when dealing with neurosurgical emergencies, a conservative approach is recommended. Using committed personal protective equipment, short-time operating procedures or minimally invasive surgery must be considered in the management of emergent patients. Resuming elective surgeries need defining measures needed to ensure patients and health-care providers' safety. Reorganizing the health-care system for telemonitoring released patients can lessen hospital visits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".