P.172 Work-up and management of asymptomatic extracranial traumatic vertebral artery injury
Bibliographic record
Abstract
Background: Extracranial traumatic vertebral artery injury (eTVAI) is common following non-penetrating head and neck trauma. Most cases are initially asymptomatic with an increased risk for stroke. Consensus is lacking regarding screening, treatment, and follow-up of asymptomatic patients with eTVAI. Our objective was to investigate national practice patterns reflecting these domains. Methods: An electronic survey was distributed via the Canadian Neurological Sciences Federation and Canadian Spine Society. Two case-based scenarios featured asymptomatic patients with eTVAI. Case 1: non-displaced cervical lateral mass fracture; angiography stratified by luminal diameter reduction. Case 2: complex C2 fracture; angiography featuring pseudoaneurysm dissection. Analysis: descriptive statistics. Results: Response Rate: 108 of 182 participants (59%), representing 20 academic institutions. Case 1: 78% of respondents would screen using CTA (97%), immediately (88%). Most respondents (97%) would initiate treatment, using aspirin (89%) for 3-6 months (46%). Case 2: 73% of respondents would screen using CTA (96%), immediately (88%). The majority of respondents (94%) would initiate treatment, using aspirin (50%) for 3-6 months (35%). Thirty-six percent of respondents would utilize endovascular therapy. In both cases, the majority of respondents would follow-up clinically or radiographically every 1-3 months, respectively. Conclusions: This study highlights consensus in Canadian practice patterns for the workup and management of asymptomatic eTVAI.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".