Coma in adult cerebral venous thrombosis: The <scp>BEAST</scp> study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Coma is an independent predictor of poor clinical outcomes in cerebral venous thrombosis (CVT). We aimed to describe the association of age, sex, and radiological characteristics of adult coma patients with CVT. METHODS: We used data from the international, multicentre prospective observational BEAST (Biorepository to Establish the Aetiology of Sinovenous Thrombosis) study. Only positively associated variables with coma with <10% missing data in univariate analysis were considered for the multivariate logistic regression model. RESULTS: Of the 596 adult patients with CVT (75.7% women), 53 (8.9%) patients suffered coma. Despite being a female-predominant disease, the prevalence of coma was higher among men than women (13.1% vs. 7.5%, p = 0.04). Transverse sinus thrombosis was least likely to be associated with coma (23.9% vs. 73.3%, p < 0.001). The prevalence of superior sagittal sinus thrombosis was higher among men than women in the coma sample (73.6% vs. 37.5%, p = 0.01). Men were significantly older than women, with a median (interquartile range) age of 51 (38.5-60) versus 40 (33-47) years in the coma (p = 0.04) and 44.5 (34-58) versus 37 (29-48) years in the non-coma sample (p < 0.001), respectively. Furthermore, an age- and superior sagittal sinus-adjusted multivariate logistic regression model found male sex (odds ratio = 1.8, 95% confidence interval [CI] = 1.0-3.4, p = 0.04) to be an independent predictor of coma in CVT, with an area under the receiver operating characteristic curve of 0.61 (95% CI = 0.52-0.68, p = 0.01). CONCLUSIONS: Although CVT is a female-predominant disease, men were older and nearly twice as likely to suffer from coma than women.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".