Treatment of asymptomatic blunt cerebrovascular injury (BCVI): a systematic review
Bibliographic record
Abstract
BACKGROUND: The management of asymptomatic blunt cerebrovascular injury (BCVI) with respect to stroke prevention and vessel healing is challenging. OBJECTIVES: The aim of this systematic review was to determine if a specific treatment results in lower stroke rates and/or improved vessel healing in asymptomatic BCVI. DATA SOURCES: An electronic literature search of MEDLINE, EMBASE, Cochrane Library, CINAHL, SCOPUS, Web of Science, and ClinicalTrials.gov performed from inception to March 2020. STUDY ELIGIBILITY CRITERIA: Studies were included if they reported on a comparison of any treatment for BCVI and stroke and/or vessel healing rates. PARTICIPANTS AND INTERVENTIONS: Adult patients diagnosed with asymptomatic BCVI(s) who were treated with any preventive medication or procedure. STUDY APPRAISAL AND SYNTHESIS METHODS: All studies were systematically reviewed and bias was evaluated by the Newcastle-Ottawa Scale. No meta-analysis was performed secondary to significant heterogeneity across studies in patient population, screening protocols, and treatment selection. The main outcomes were stroke and healing rate. RESULTS: Of 8781 studies reviewed, 19 reported on treatment effects for asymptomatic BCVI and were included for review. Any choice of medical management was better than no treatment, but no specific differences between choice of medical management and stroke outcomes were found. Vessel healing was rare and the majority of healed vessels were following low-grade injuries. LIMITATIONS: Majority of the included studies were retrospective and at high risk of bias. CONCLUSIONS OR IMPLICATIONS OF KEY FINDINGS: Asymptomatic BCVI should be treated medically using a consistent, local protocol. High-quality studies on the effect of individual antithrombotic agents on stroke rates and vessel healing for asymptomatic BCVI are required.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".