Bibliographic record
Abstract
ABSTRACT: The low incidence of blunt cerebrovascular injury (BCVI) reported in pediatric studies (<1%) might be related to an underreporting due to both the absence of current screening guidelines and the use of inadequate imaging techniques. This research is a review of the literature limited to the last 5 years (2017-2022) about the approach and management of BCVI in pediatrics. The strongest predictors for BCVI were the presence of basal skull fracture, cervical spine fracture, intracranial hemorrhage, Glasgow Coma Scale score less than 8, mandible fracture, and injury severity score more than 15. Vertebral artery injuries had the highest associated stroke rate of any injury type at 27.6% (vs 20.1% in carotid injury). The sensitivity of the well-established screening guidelines of BCVI varies when applied to the pediatric population (Utah score - 36%, 17%, Eastern Association for the Surgery of Trauma (EAST) guideline - 17%, and Denver criteria - 2%). A recent metaanalysis of 8 studies comparing early computed tomographic angiogram (CTA) to digital subtraction angiography for BCVI detection in adult trauma patients demonstrated high variability in the sensitivity and specificity of CTA across centers. Overall, CTA was found to have a high specificity but low sensitivity for BCVI. The role of antithrombotic as well as the type and duration of therapy remain controversial. Studies suggest that systemic heparinization and antiplatelet therapy are equally effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".