The hospitalization rate of cerebral venous sinus thrombosis before and during COVID-19 pandemic era: A single-center retrospective cohort study
Bibliographic record
Abstract
Abstract Background There are several reports of the association between SARS-CoV-2infection (COVID-19) and cerebral venous sinus thrombosis (CVST). In this study, we aimed to compare the hospitalization rate of CVST before and during the COVID-19 pandemic (before vaccination program). Methods In this retrospective cohort study, the hospitalization rate of adult CVST patients in Namazi hospital, a tertiary referral center in the south of Iran, was compared in two periods of time. We defined March 2018 to March 2019 as the pre-COVID-19 period and March 2020 to March 2021 as the COVID-19 period. Results 50 and 77 adult CVST patients were hospitalized in the pre-COVID-19 and COVID-19 periods, respectively. The crude CVST hospitalization rate increased from 14.33 in the pre-COVID-19 period to 21.7 per million in the COVID-19 era (P=0.021). However, after age and sex adjustment, the incremental trend in hospitalization rate was not significant (95% CrI: -2.2, 5.14). Patients > 50-year-old were more often hospitalized in the COVID-19 period. (P=0.042) SARS-CoV-2 PCR test was done in 49.3% out of all COVID-19 period patients, which were positive in 6.5%. Modified Rankin Scale (mRS) score ≥3 at three-month follow-up was associated with age (P=0.015) and malignancy (P=0.014) in pre-COVID period; and was associated with age (P=0.025), altered mental status on admission time (P<0.001), malignancy (P=0.041) and COVID-19 infection (P=0.008) in COVID-19 period. Conclusion Since there was a more dismal outcome in COVID-19 associated CVST, a high index of suspicion for CVST among COVID-19 positive is recommended.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".