Traumatic Intracranial Injury in Intoxicated Patients With Minor Head Trauma
Bibliographic record
Abstract
OBJECTIVES: Studies focusing on minor head injury in intoxicated patients report disparate prevalences of intracranial injury. It is unclear if the typical factors associated with intracranial injury in published clinical decision rules for computerized tomography (CT) acquisition are helpful in differentiating patients with and without intracranial injuries, as intoxication may obscure particular features of intracranial injury such as headache and mimic other signs of head injury such as altered mental status. This study aimed to estimate the prevalence of intracranial injury following minor head injury (Glasgow Coma Scale [GCS] score ≥14) in intoxicated patients and to assess the performance of established clinical decision rules in this population. METHODS: This was a prospective cohort study of consecutive intoxicated adults presenting to the emergency department (ED) following minor head injury. Historical and physical examination features included those from the Canadian CT Head Rule, National Emergency X-Radiography Utilization Study (NEXUS), and New Orleans Criteria. All patients underwent head CT. RESULTS: A total of 283 patients were enrolled, with a median age of 40 years (interquartile range [IQR] = 28 to 48 years) and median alcohol concentration of 195 mmol/L (IQR = 154 to 256 mmol/L). A total of 238 of 283 (84%) were male, and 225 (80%) had GCS scores of 15. Clinically important injuries (injuries requiring admission to the hospital or neurosurgical follow-up) were identified in 23 patients (8%; 95% confidence interval [CI] = 5% to 12%); one required neurosurgical intervention (0.4%, 95% CI = 0% to 2%). Loss of consciousness and headache were associated with clinically important intracranial injury on CT. The Canadian CT Head Rule had a sensitivity of 70% (95% CI = 47% to 87%) and NEXUS criteria had a sensitivity of 83% (95% CI = 61% to 95%) for clinically important injury in intoxicated patients. CONCLUSIONS: In this study, the prevalence of clinically important injury in intoxicated patients with minor head injury was significant. While the presence of the common features associated with intracranial injury in nonintoxicated patients should raise clinical suspicion for intracranial injury in intoxicated patients, the Canadian CT Head Rule and NEXUS criteria do not have adequate sensitivity to be applied in intoxicated patients with minor head injury.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".