Should a Head-Injured Child Receive a Head CT Scan? A Systematic Review of Clinical Prediction Rules
Bibliographic record
Abstract
CONTEXT: Given radiation- and sedation-associated risks, there is uncertainty about which children with head trauma should receive cranial computed tomography (CT) scanning. A high-quality and high-performing clinical prediction rule may reduce this uncertainty. OBJECTIVE: To systematically review the quality and performance of published clinical prediction rules for intracranial injury in children with head injury. METHODS: Medline and Embase were searched in December 2008. Studies were selected if they included clinical prediction rules involving children aged 0 to 18 years with a history of head injury. Prediction-rule quality was assessed by using 14 previously published items. Prediction-rule performance was evaluated by rule sensitivity and the predicted frequency of CT scanning if the rule was used. RESULTS: A total of 3357 titles and abstracts were assessed, and 8 clinical prediction rules were identified. For all studies, the rule derivations were reported; no study validated a rule in a separate population or assessed its impact in actual practice. The rules differed considerably in population, predictors, outcomes, methodologic quality, and performance. Five of the rules were applicable to children of all ages and severities of trauma. Two of these were high quality (>or=11 of 14 quality items) and had high performance (lower confidence limits for sensitivity >0.95 and required or=13). One of these had high quality (11 of 14 quality items) and high performance (lower confidence limit for sensitivity = 0.94 and required 13% to undergo CT). Four of the 8 rules were applicable to young children, but none exhibited adequate quality or performance. CONCLUSIONS: Eight clinical prediction-rule derivation studies were identified. They varied considerably in population, methodologic quality, and performance. Future efforts should be directed toward validating rules with high quality and performance in other populations and deriving a high-quality, high-performance rule for young children.
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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 | 0.026 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".