Computed Tomography Use for Adults With Head Injury: Describing Likely Avoidable Emergency Department Imaging Based on the Canadian CT Head Rule
Bibliographic record
Abstract
BACKGROUND: Millions of head computed tomography (CT) scans are ordered annually, but the extent of avoidable imaging is poorly defined. OBJECTIVES: The objective was to determine the prevalence of likely avoidable CT imaging among adults evaluated for head injury in 14 community emergency departments (EDs) in Southern California. METHODS: We conducted an electronic health record (EHR) database and chart review of adult ED trauma encounters receiving a head CT from 2008 to 2013. The primary outcome was discordance with the Canadian CT Head Rule (CCHR) high-risk criteria; the secondary outcome was use of a neurosurgical intervention in the discordant cohort. We queried systemwide EHRs to identify CCHR discordance using criteria identifiable in discrete data fields. Explicit chart review of a subset of discordant CTs provided estimates of misclassification bias and assessed the low-risk cases who actually received an intervention. RESULTS: Among 27,240 adult trauma head CTs, EHR data classified 11,432 (42.0%) discordant with CCHR recommendation. Subsequent chart review showed that the designation of discordance based on the EHR was inaccurate in 12.2% (95% confidence interval [CI] = 5.6% to 18.8%). Inter-rater reliability for attributing CCHR concordance was 95% (κ = 0.86). Thus, we estimate that 36.8% of trauma head CTs were truly likely avoidable (95% CI = 34.1% to 39.6%). Among the likely avoidable CT group identified by EHR, only 0.1% (n = 13) received a neurosurgical intervention. Chart review showed none of these were actually "missed" by the CCHR, as all 13 were misclassified. CONCLUSION: About one-third of head CTs currently performed on adults with head injury may be avoidable by applying the CCHR. Avoidance of CT in such patients is unlikely to miss any important injuries.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 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".