Minor Head Injury: CT-based Strategies for Management—A Cost-effectiveness Analysis
Bibliographic record
Abstract
PURPOSE: To compare the cost-effectiveness of using selective computed tomographic (CT) strategies with that of performing CT in all patients with minor head injury (MHI). MATERIALS AND METHODS: The internal review board approved the study; written informed consent was obtained from all interviewed patients. Five strategies were evaluated, with CT performed in all patients with MHI; selectively according to the New Orleans criteria (NOC), Canadian CT head rule (CCHR), or CT in head injury patients (CHIP) rule; or in no patients. A decision tree was used to analyze short-term costs and effectiveness, and a Markov model was used to analyze long-term costs and effectiveness. n-Way and probabilistic sensitivity analyses and value-of-information (VOI) analysis were performed. Data from the multicenter CHIP Study involving 3181 patients with MHI were used. Outcome measures were first-year and lifetime costs, quality-adjusted life-years, and incremental cost-effectiveness ratios. RESULTS: Study results showed that performing CT selectively according to the CCHR or the CHIP rule could lead to substantial U.S. cost savings ($120 million and $71 million, respectively), and the CCHR was the most cost-effective at reference-case analysis. When the prediction rule had lower than 97% sensitivity for the identification of patients who required neurosurgery, performing CT in all patients was cost-effective. The CHIP rule was most likely to be cost-effective. At VOI analysis, the expected value of perfect information was $7 billion, mainly because of uncertainty about long-term functional outcomes. CONCLUSION: Selecting patients with MHI for CT renders cost savings and may be cost-effective, provided the sensitivity for the identification of patients who require neurosurgery is extremely high. Uncertainty regarding long-term functional outcomes after MHI justifies the routine use of CT in all patients with these 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".