182: Evaluation of a Clinical Score for Skull Radiography of Young Children with Isolated Head Trauma
Bibliographic record
Abstract
A clinical score was reported in 2010 by Bin et al to identify head-injured infants that are at higher risk of skull fracture. To determine the criterion validity of the clinical score to identify skull fracture among children younger than two years old with head trauma and no need for head tomography. A prospective cohort study was conducted in two pediatric emergency departments. Participants were all children younger than 24 months who sustained a head trauma and for whom head tomography was not highly recommended according to the PECARN head CT scan rule. The exposure of interest was the clinical score (from 0 to 8) composed of the age of the patient, the size and location of the hematoma. The previous study suggested that a score higher than two would be predictive of skull fracture. The primary outcome was the presence of a skull fracture according to radiological report. All participants were initially evaluated by a physician using a standardized datasheet before radiological evaluation. Skull radiography ordering was left at the primary physician's discretion. The primary analysis was the association between the clinical score and presence of a skull fracture. It was estimated that a sample of 50 cases of fracture would provide a width of ±0.05 if the sensitivity of the score was higher than 0.90. A total of 765 patients were recruited during the study period. Among them, 271 had a radiological evaluation and 50 had a skull fracture. Most children had low clinical score but 209 had a score >2. A clinical score >2 points resulted in a sensitivity of 0.86 (95% CI 0.74 to 0.93) and a specificity 0.77 (95% CI 0.74 to 0.80). The clinical decision score demonstrated a moderated sensitivity to identify children at risk of skull fracture. Further studies are required to derive a useful clinical decision rule for young children with head trauma.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".