Regional Variability in the Use of CT for Patients with Suspected Mild Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To investigate the use of computed tomography (CT) scans in patients with suspected acute mild traumatic brain injury (mTBI) presenting to emergency departments. METHOD: 850 potential mTBI cases were identified through reviews of three months of health records from nine selected emergency departments across the province of Ontario. Records for review were selected using the International Classification of Disease, 9th revision, Clinical Modification codes and Injury codes. RESULTS: Patients who received head CT were significantly older (p<0.01), had documented loss-of-consciousness (LOC) &/or Post-Traumatic Amnesia (PTA) (p<0.001), documented nausea (p<0.01), documented vomiting (p<0.001), abnormal neurological exam results (p<0.01), had visited an urban center (p<0.001), and/or arrived by ambulance (p<0.001). The significant predictors of CT scan prescription (in a forward stepwise logistic regression) were urban location of hospital (OR=5.14; p<0.001), LOC &/or PTA (OR=4.83; p< or =0.001), vomiting (OR=2.56; p< or =0.01), arrival by ambulance (OR=2.15; p< or =0.001), nausea (OR=1.92; p< or =0.02) and older age (OR=1.02; p< or =0.01). CONCLUSION: These data extend our knowledge regarding the use of CT during acute diagnosis and management of suspected mTBI patients. In addition to confirming previously reported risk factors of intracranial complication, geographical location of hospital and arrival mode were found to be significant predictors of CT use. The results suggest that the management patterns for acute mTBI are inconsistent. The implications of this are discussed.
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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 | 0.001 | 0.010 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".