Derivation and validation of a clinical decision rule to identify young children with skull fracture following isolated head trauma
Bibliographic record
Abstract
BACKGROUND: There is no clear consensus regarding radiologic evaluation of head trauma in young children without traumatic brain injury. We conducted a study to develop and validate a clinical decision rule to identify skull fracture in young children with head trauma and no immediate need for head tomography. METHODS: We performed a prospective cohort study in 3 tertiary care emergency departments in the province of Quebec. Participants were children less than 2 years old who had a head trauma and were not at high risk of clinically important traumatic brain injury (Glasgow Coma Scale score < 15, altered level of consciousness or palpable skull fracture). The primary outcome was skull fracture. For each participant, the treating physician completed a standardized report form after physical examination and before radiologic evaluation. The decision to order skull radiography was at the physician's discretion. The clinical decision rule was derived using recursive partitioning. RESULTS: A total of 811 patients (49 with skull fracture) were recruited during the derivation phase. The 2 predictors identified through recursive partitioning were parietal or occipital swelling or hematoma and age less than 2 months. The rule had a sensitivity of 94% (95% confidence interval [CI] 83%-99%) and a specificity of 86% (95% CI 84%-89%) in the derivation phase. During the validation phase, 856 participants (44 with skull fracture) were recruited. The rule had a sensitivity of 89% and a specificity of 87% during this phase. INTERPRETATION: The clinical decision rule developed in this study identified about 90% of skull fractures among young children with mild head trauma who had no immediate indication for head tomography. Use of the rule would have reduced the number of radiologic evaluations by about 60%.
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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 | 0.027 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".