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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".