Clinical Decision Rules for Adults With Minor Head Injury: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: There are many clinical decision rules for adults with minor head injury, but it is unclear how they compare in terms of diagnostic accuracy. This study aimed to systematically identify clinical decision rules for adults with minor head injury and compare the estimated diagnostic accuracies for any intracranial injury and injury requiring neurosurgical intervention. METHODS: Several electronic bibliographic databases covering biomedical, scientific, and gray literature were searched from inception to March 2010. At least two independent reviewers determined the eligibility of cohort studies that described a clinical decision rule to identify adults with minor head injury (Glasgow Coma Scale score, 13-15) at risk of intracranial injury or injury requiring neurosurgical intervention. RESULTS: Twenty-two relevant studies were identified. Differences existed in patient selection, outcome definition, and reference standards used. Nine rules stratified patients into high- and moderate-risk categories (to identify neurosurgical or nonsurgical intracranial lesions). The Canadian Computed Tomography Head Rule (CCHR) high-risk criteria have sensitivity of 99% to 100% with specificity of 48% to 77% for injury requiring neurosurgical intervention. Other rules such as New Orleans criteria, National Emergency X-Radiography Utilization Study II, Neurotraumatology Committee of the World Federation of Neurosurgical Societies, Scandinavian, and Scottish Intercollegiate Guidelines Network produce similar sensitivities for injury requiring neurosurgical intervention but with lower and more variable specificity values. DISCUSSION: The most widely researched decision rule is the CCHR, which has consistently shown high sensitivity for identifying injury requiring neurosurgical intervention with an acceptable specificity to allow considered use of cranial computed tomography. No other decision rule has been as widely validated or demonstrated as acceptable results, but its exclusion criteria make it difficult to apply universally.
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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 | 0.021 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".