Outcomes after Traumatic Brain Injury with and Without Computed Tomography
Bibliographic record
Abstract
Our recent improved understanding of traumatic brain injury (TBI) comes largely from cohort studies of TBI patients with indication for computed tomography (CT). Using CT head as an inclusion criterion may overestimate poor outcomes after TBI with Glasgow Coma Scale (GCS) 13–15. We aimed to compare outcomes after TBI in adults who had a head CT scan (with negative findings) versus those who had no CT when presenting to an emergency department. This was a secondary analysis of a trial that recruited adults with GCS = 13–15 after TBI in Vancouver, Canada. We included 493 participants (18–69 years, 54% female), after removing n = 19 with traumatic abnormalities on CT (intracranial and/or skull fracture). Outcomes were Glasgow Outcome Scale Extended (GOSE), Rivermead Post-Concussion Symptoms Questionnaire (RPQ), Patient Health Questionnaire (PHQ)−9, and generalized anxiety disorder (GAD)−7 at 6 months post-injury. Over half (55%) of participants received a CT. At 6 months, 55% of participants with CT and 49% without CT had functional limitations on GOSE; 32% with CT and 33% without CT reported severe post-concussion symptoms (RPQ ≥16); 26% (with CT) and 28% (without CT) screened positive for depression (PHQ-9 ≥ 10), and 25% (with CT) and 28% (without CT) screened positive for anxiety (GAD-7 ≥ 8). In regression adjusted for personal variables, participants with CT had somewhat higher odds of worse functioning (ordinal GOSE; 1.4, 95% CI 1.0–2.0) but similar odds of severe post-concussion symptoms (1.1, 95% CI: 0.7–1.7), and depression (1.1, 95% CI: 0.7–1.7) and anxiety (1.0, 95% CI: 0.6–1.5) symptoms. Adults with and without head CT have mostly comparable outcomes from TBI with GCS = 13–15. Requiring CT by clinical indication for study entry may not create problematic selection bias for outcome research.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".