P.013 Delaying CT Venograms in patients with skull base fractures improves the sensitivity of screening protocols: report of a case with delayed onset
Bibliographic record
Abstract
Background: Cerebral venous sinus thrombosis (CVST) is a possible complication of closed head trauma with reported devastating outcomes. Its incidence however is unclear but believed to be frequent in patients with skull base fractures. The natural history of this under-recognized entity is not yet described, but a sensitive screening method is required to definitively address this question. Methods: Case report with literature review. Results: We report the case of a patient that sustained a severe head injury as the pedestrian in a motor vehicle accident. The patient required required a craniectomy to evacuate an acute subdural hematoma. Post-operatively, a CT venogram was performed and showed patent venous sinuses. A few days later, a double order resulted in a CTV being repeated erroneously but revealed the interval development of significant thrombosis of his left transverse sinus extending to his left internal jugular vein. We report on this patient’s outcome and follow-up. Conclusions: Further understanding of sinus thrombosis in the setting of TBI is warranted. The natural history is unclear, and most cases are discovered once symptomatic or after developing complications. Our case shows that current protocols have the potential of missing significant cases and study into the optimal timing of imaging is necessary.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".