Development of Guidelines for Skeletal Survey in Young Children With Fractures
Bibliographic record
Abstract
OBJECTIVE: To develop guidelines for performing initial skeletal survey (SS) in children <24 months old with fractures, based on available evidence and collective judgment of experts from diverse pediatric specialties. METHODS: Following the Rand/UCLA Method, a multispecialty panel of 13 experts applied evidence from a literature review combined with their own expertise in rating the appropriateness of performing an SS for 525 clinical scenarios involving fractures in children <24 months old. After discussion on the initial ratings, panelists re-rated SS appropriateness for 240 revised scenarios and deemed that SSs were appropriate in 191 scenarios. The panelists then assessed in which of those 191 scenarios SSs were not only appropriate, but also necessary. RESULTS: Panelists agreed that SS is "appropriate" for 191 (80%) of 240 scenarios rated and "necessary" for 175 (92%) of the appropriate scenarios. Skeletal survey is necessary if a fracture is attributed to abuse, domestic violence, or being hit by a toy. With few exceptions, SS is necessary in children without a history of trauma. In children <12 months old, SS is necessary regardless of the fracture type or reported history, with rare exceptions. In children 12 to 23 months old, the necessity of obtaining SS is dependent on fracture type. CONCLUSIONS: A multispecialty panel reached agreement on multiple clinical scenarios for which initial SS is indicated in young children with fractures, allowing for synthesis of clinical guidelines with the potential to decrease disparities in care and increase detection of abuse.
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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 | 0.075 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".