Cerebral Sinovenous Thrombosis Post Head Injury - 10 Year Experience in Children.
Bibliographic record
Abstract
Abstract Cerebral sinovenous thrombosis (CSVT) post head injury is infrequently diagnosed in children. We retrospectively reviewed 323 patients with histories of head injury and a computerized tomography scan of the head from June 1996 to June 2005. Twelve patients (3.7%) were diagnosed with CSVT. The mean age was 6 years (range from 2 to 15 years). Male to female ratio was 10:2. The mechanism of injury included motor vehicle accidents in 5 patients, and falls in 7 patients. Nine patients had minor head injuries (GCS> 8) whereas 3 patients had severe head injuries (GCS<8). The most common presenting symptom was vomiting in 5 patients and other less common symptoms including: headache, seizure and irritability. The commonest locations of thrombosis were sigmoid and transverse sinus. Four patients had extensive thrombus involving more than 3 sinuses. Of the 12 patients, 9 (75%) patients had skull fractures and 6 (50%) had epidural hematomas in association with CSVT. Seven patients (58%) received anticoagulant treatment. The duration of anticoagulant therapy was from 2 days to 6 months. Anticoagulation was discontinued in 3 patients (25%) due to extension of CNS bleeding. All patients receiving anticoagulation were therapeutic with an anti-factor Xa level of 0.5–1 units/mL on enoxaparin, 0.35–0.7 units/mL on standard heparin and INR level of 2–3 on Coumadin. There was complete resolution in 6 patients with the imaging done in a mean followup of 78.5 days, partial resolution in 3 patients with the mean followup imaging in 95.6 days, unchanged status in 1 patient 6 days after the diagnosis and no followup imaging in 2 patients. Six patients had thrombophilic work-up and all were normal. Of the 7 patients with full recovery, 3 patients did not receive anticoagulation. Four patients had seizure and headache, left hemiparesis, panhypopituitarism and optic atrophy, and attention deficit disorder, respectively. In conclusion, the prevalence of CSVT post head injury in children was approximately 3.7%. The etiology of CSVT in this situation might be associated with skull fracture and hematoma, which could compress the cerebral sinus. Large prospective studies involving multiple centers are required to ascertain whether prothrombotic investigations are necessary and if anticoagulation improves neurologic outcome in patients with CVST post head injury.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".