Clinical utility of a screening protocol for blunt cerebrovascular injury using computed tomography angiography
Bibliographic record
Abstract
OBJECTIVE Blunt cerebrovascular injury (BCVI) occurs in approximately 1% of the blunt trauma population and may lead to stroke and death. Early vascular imaging in asymptomatic patients at high risk of having BCVI may lead to earlier diagnosis and possible stroke prevention. The objective of this study was to determine if the implementation of a formalized asymptomatic BCVI screening protocol with CT angiography (CTA) would lead to improved BCVI detection and stroke prevention. METHODS Patients with vascular imaging studies were identified from a prospective trauma registry at a single Level 1 trauma center between 2002 and 2008. Detection of BCVI and stroke rates were compared during the 3-year periods before and after implementation of a consensus-based asymptomatic BCVI screening protocol using CTA in 2005. RESULTS A total of 5480 patients with trauma were identified. The overall BCVI detection rate remained unchanged postprotocol compared with preprotocol (0.8% [24 of 3049 patients] vs 0.9% [23 of 2431 patients]; p = 0.53). However, postprotocol there was a trend toward a decreased risk of stroke secondary to BCVI on a trauma population basis (0.23% [7 of 3049 patients] vs 0.53% [13 of 2431 patients]; p = 0.06). Overall, 75% (35 of 47) of patients with BCVI were treated with antiplatelet agents, but no patient developed new or progressive intracranial hemorrhage despite 70% of these patients having concomitant traumatic brain injury. CONCLUSIONS The results of this study suggest that a CTA screening protocol for BCVI may be of clinical benefit with possible reduction in ischemic complications. The treatment of BCVI with antiplatelet agents appears to be safe.
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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 | 0.009 | 0.044 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".