Natural history of cerebral vein thrombosis: a systematic review
Bibliographic record
Abstract
UNLABELLED: Cerebral vein thrombosis (CVT) has been considered, until a few years ago, an uncommon disease with significant long-term morbidity and high mortality rate. New noninvasive diagnostic techniques have increased the frequency with which this disease is diagnosed; despite this, there continues to be little data on its natural history. The objectives of this study were to evaluate the mortality rate, the rate of disability at long-term follow-up, and the incidence of recurrence after a first episode of CVT; to determine clinical and radiologic predictors of death and dependence; and to identify possible risk factors for recurrence. ( DATA SOURCE: MEDLINE and EMBASE databases, reference lists of selected articles and authors' libraries.) Nineteen studies were identified. Mortality rate during peri-hospitalization period is 5.6% (range, 0%-15.2%) and 9.4% (range, 0%-39%) at the end of follow-up period. Eighty-eight percent of surviving patients recover completely or have only a mild functional or cognitive deficit. Two thirds of patients with CVT recanalized within the first few months after presentation, and 2.8% (range, 0%-11.7%) had objectively confirmed recurrence. We conclude that patients with CVT have a low risk of death and that most patients have a good long-term prognosis.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".