Delayed Identification of Pediatric Abuse-Related Fractures
Bibliographic record
Abstract
OBJECTIVES: Because physicians may have difficulty distinguishing accidental fractures from those that are caused by abuse, abusive fractures may be at risk for delayed recognition; therefore, the primary objective of this study was to determine how frequently abusive fractures were missed by physicians during previous examinations. A secondary objective was to determine clinical predictors that are associated with unrecognized abuse. METHODS: Children who were younger than 3 years and presented to a large academic children's hospital from January 1993 to December 2007 and received a diagnosis of abusive fractures by a multidisciplinary child protective team were included in this retrospective review. The main outcome measures included the proportion of children who had abusive fractures and had at least 1 previous physician visit with diagnosis of abuse not identified and predictors that were independently associated with missed abuse. RESULTS: Of 258 patients with abusive fractures, 54 (20.9%) had at least 1 previous physician visit at which abuse was missed. The median time to correct diagnosis from the first visit was 8 days (minimum: 1; maximum: 160). Independent predictors of missed abuse were male gender, extremity versus axially located fracture, and presentation to a primary care setting versus pediatric emergency department or to a general versus pediatric emergency department. CONCLUSIONS: One fifth of children with abuse-related fractures are missed during the initial medical visit. In particular, boys who present to a primary care or a general emergency department setting with an extremity fracture are at a particularly high risk for delayed diagnosis.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".