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Background: The COVID-19 pandemic committees of all countries restricted face-to-face interactions. This study aimed to determine how the pandemic changed the research output for many neurosurgeons while highlighting how social media may have been used as a contactless platform to maintain research productivity during these times., Methods: A cross-sectional, descriptive, 24-item, and non-randomized online survey was applied worldwide, and shared using social media platforms and emails. The questions mainly focused on comparing the results of the pre-pandemic period to the pandemic period (after March 2020)., Results: A total of 202 respondents from 60 different countries responded to the survey. Interest in neurosurgical education increased from 24% to 76%, while the topic of epidemiology gained interest from 28% to 72% when the pre-pandemic era was compared to the pandemic era. Preference for prospective studies decreased from 66% to 34%, while interest in retrospective studies increased from 39% to 61%. In evaluating publication types, the preference for reviews increased from 36% to 64%. Sixty-two percent of the respondents stated they had concerns over delays in individual contributions/lack of accountability. These concerns were followed by problems with theft of intellectual property/data and authorship disputes. Forty-one percent believed that the support of extra hands on a load-heavy project was the most powerful benefit of social media collaboration. Those who reported increased publications during the pandemic were also more likely to collaborate using social media (P = 0.030)., Conclusions: During the pandemic, social media collaborations helped increase research output for neurosurgeons., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective: The method for predicting the risk of intubation in patients with coronavirus disease 2019 (COVID-19) is yet to be standardized. This study aimed to introduce a new disease prognosis scoring model that may predict the intubation risk based on the symptoms, signs, and laboratory tests of patients hospitalized with the diagnosis of COVID-19., Method: This cross-sectional retrospective study analyzed the intubation status of 733 patients hospitalized with COVID-19 diagnosis between March and December 2020 at Ondokuz Mayıs University Faculty of Medicine, Turkey, based on 33 variables. Binary logistic regression analysis was used to select the variables that significantly affect intubation, which constitute the risk factors. The Chi-square Automatic Interaction Detection algorithm, one of the data mining methods, was used to determine the threshold values of the important variables for intubation classification., Results: The following variables found were mostly associated with intubation: C-reactive protein, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, age, lymphocyte count, and malignancy. The logistic function based on these variables correctly predicted 81.13% of intubated (sensitivity), 99.52% of nonintubated (specificity), and 96.86% of both intubated and nonintubated (accurate classification rate) patients. The scoring model revealed the following risk statuses for the intubated patients: very high risk, 75.47%; moderate risk, 20.75%; and very low risk, 3.77%., Conclusions: On the basis of certain variables measured at admission, the OTO-COVID-19 scoring model may help clinicians identify patients at the risk of intubation and subsequently provide a prompt and effective treatment at the earliest.

The aim of the study was to examine the genotype-allele determination of CYP1A2 rs2069514 and rs762551 polymorphisms in patients with mild and severe COVID-19 and to determine their effectiveness as prognostic criteria in COVID-19. The study consists of 60 patients who were hospitalized in intensive care or outpatient treatment due to COVID-19 in Istanbul NP Brain Hospital between 2020–2021. Genotyping was conducted by Real-Time PCR. Age (p