Management of blunt cerebrovascular injuries at a Canadian level 1 trauma centre: Are we meeting the grade?
Bibliographic record
Abstract
BACKGROUND: Untreated blunt cerebrovascular injuries (BCVIs) are associated with high rates of death and disability due to stroke. We assessed alignment of clinical practice at our centre with current recommendations for management of BCVIs and examined rates of new and recurrent in-hospital stroke. METHODS: We retrospectively reviewed the BC Trauma Registry to identify all adult (age > 18 yr) patients with trauma with BCVIs at the largest level 1 trauma centre in British Columbia, Canada, from Apr. 1, 2013, to Mar. 31, 2018. We evaluated the registry, hospital databases and patient charts to assess alignment with guidelines for early initiation of appropriate antithrombotic therapy and follow-up imaging, and to ascertain short-term outcomes. RESULTS: A total of 186 patients met the inclusion criteria. Just over half of BCVIs (97 [52.2%]) were Biffl grade 1-2. The majority of patients were treated with acetylsalicylic acid monotherapy (144/162 [88.9%]) or low-molecular-weight heparin (2/162 [1.2%]). Although guidelines recommend repeat imaging at 7-10 days to reassess the injury and guide duration of therapy, only 61/171 patients (35.7%) underwent repeat imaging within 7 days. Neuroimaging within 3 months after injury showed brain infarction in 29 patients (15.6%). CONCLUSION: Antithrombotic therapy was initiated in the majority of eligible patients with BCVIs, but completion of follow-up imaging and documentation of clear outpatient care plans were suboptimal. This finding shows the need for routine multidisciplinary management to facilitate standardization of care for this complex population.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".