CT findings associated with poor outcomes in emergency patients with traumatic brain injury
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI) is responsible for 90% of traumatic brain injuries and necessitates computed tomography (CT) imaging to detect intracranial lesions. Many mTBI patients recover fully, some develop adverse outcomes, and the prognostic value of pathological CT findings in this population is uncertain. This review aimed to systematically review and identify pathological CT features associated with poor clinical outcomes in patients with mTBI. A systematic search of PubMed, Scopus, Web of Science, and Google Scholar was performed to identify relevant studies published between the years 2017 and 2025. Eligible studies included observational designs that assessed patients with mTBI (Glasgow Coma Scale 13-15) and reported CT findings correlated with clinical outcomes. Data extraction and quality assessment using the Newcastle-Ottawa Scale were performed by two reviewers. A total of 11 studies were included. The most commonly reported CT findings include contusions, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, and skull fractures. Temporal and frontal contusions were associated with poor functional outcomes. Some studies reported low benefits from repeated CT in stable patients. In high-risk groups, radiological progression rarely influenced management. Pathological CT findings in mTBI, mainly contusions and hemorrhagic lesions, are associated with increased risk of adverse outcomes.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".