Dating of Bruises in Children: An Assessment of Physician Accuracy
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To determine whether physicians can estimate accurately the age of an accidental bruise on direct physical examination. METHODS: Children who presented to the emergency department of a children's hospital with accidental bruises of known age and origin had demographic data and information about their injury recorded. History-blinded emergency pediatricians, other physicians, and trainees (fellows, residents, and medical students) independently examined the bruised area and recorded injury characteristics and age estimation and ranked characteristics that influenced their estimation. RESULTS: Fifty children with accidental bruises were enrolled. Emergency pediatricians' accuracy of age estimation within 24 hours of actual age was 47.6%. Individual emergency pediatrician's accuracy ranged from 0% to 100%, and the interobserver reliability was poor (kappa = -0.03). Accuracy within 24 hours of actual age was 29.4% for other physicians and 36.8% for trainees, which was similar to the emergency pediatricians. Observers reported using color primarily to estimate age, followed by tenderness and then swelling; however, none of these factors was significantly correlated with accuracy. CONCLUSIONS: Physician estimates of bruise age are highly inaccurate within 24 hours of the actual age of the injury. Large individual variability and poor interrater reliability also suggest that caution must be used when interpreting these estimates. This study supports earlier studies, urging extreme caution in estimating bruise age, even when such estimates are based on direct examination of the injured area.
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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) | 0.000 | 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 it