Derivation of the Quebec Brain Injury Categories for complicated mild traumatic brain injuries
Bibliographic record
Abstract
OBJECTIVE: Approximately 10% of patients with mild traumatic brain injury (TBI) present with intracranial bleeding, and only 3.5% eventually require neurosurgical intervention, which often necessitates interhospital transfer. Better guidelines and recommendations are needed to manage complicated mild TBI in the emergency department (ED). The main objective of this study was to derive a clinical decision rule, the Quebec Brain Injury Categories (QueBIC), to predict the risk of adverse outcomes for complicated mild TBI in the ED. The secondary objective was to compare the QueBIC's performance with those of other existing guidelines. METHODS: The authors conducted a retrospective multicenter cohort study in 3 level I trauma centers. Consecutive patients with complicated mild TBI (Glasgow Coma Scale [GCS] score 13-15) who were aged ≥ 16 years were included. The primary outcome was a combination of neurosurgical intervention, mild TBI-related death, and clinical deterioration. Statistical analyses included set covering machine analyses. RESULTS: In total, 477 patients were included in the study. The mean age was 62.9 years, and 68.1% were male. The algorithm classified patients into three risk categories (low, moderate, and high risk). The high-risk group (128 patients) (subdural hemorrhage [SDH] width > 7 mm or any midline shift) presented a sensitivity of 84% (95% CI 71%-93%) and a specificity of 80% (95% CI 76%-84%) to detect neurosurgical intervention and mild TBI-related death, leaving 8 undetected cases. Patients in the moderate-risk group (169 patients) had at least 1 variable: SDH width > 4 mm, initial GCS score ≤ 14, > 1 intraparenchymal hemorrhage, or intraparenchymal hemorrhage width > 4 mm. The combined QueBIC high- and moderate-risk category had a sensitivity of 100% (95% CI 63%-100%) and a specificity of 53% (95% CI 47%-58%) to detect mild TBI-related death or neurosurgical intervention. The sensitivity and specificity values for clinical deterioration when no death or neurosurgical intervention occurred were 81% (95% CI 64%-93%) and 44% (95% CI 39%-49%), respectively. The remaining 180 patients (37.7%) did not meet any high-risk or moderate-risk criteria and were considered low risk. None had neurosurgical intervention or mild TBI-related death. Only 6 (3.3%) low-risk patients showed clinical deterioration. CONCLUSIONS: QueBIC is a safe and effective tool to guide the management of patients presenting to the ED with complicated mild TBI. It accurately identifies patients at low risk for specialized neurotrauma or neurosurgical care. Further validation is required before its use in EDs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".