390 Adequacy of the diagnostic workup of suspected child physical abuse: a study in the Paris metropolitan area
Bibliographic record
Abstract
Objectives An adequate diagnostic workup of suspected child physical abuse (CPA) is of paramount importance to minimize false-negative and false-positive cases. We aimed to evaluate diagnostic practices in a French regional area. Methods We systematically reviewed the electronic medical records in the Clinical Data Warehouse of all infants ≤2 years old hospitalized from January 1, 2018 to December 31, 2019 in the eight paediatric tertiary hospitals in the Paris metropolitan area with an end-of-stay diagnosis of CPA. We compared the frequency of the diagnostic tests performed to that expected according to current national and international clinical guidelines and the type of injuries investigated. Results Among the 100 included infants (median age 5 months), 61% had intracranial injuries, 34% skin injuries, 30% skeletal injuries and 8% died. According to guidelines, an initial radiological skeletal survey (RSS), a head computed tomography or a head magnetic resonance imaging and an eye fundus examination were expected in all infants and were only performed in 88%, 99% and 95% of them, respectively. Follow-up RSS or bone scintigraphy were expected on a case-by-case basis, particularly in the 56 children with negative initial RSS but were performed in 0%. Investigation regarding bone metabolism was expected in the 30 children with ≥1 unexplained fracture(s) and was performed in only 60%. Investigation of primary hemostasis and coagulation was expected in the 79 children with bruises or internal bleeding and were performed in only 78%. Conclusions Practices observed in the Paris metropolitan area for the diagnostic workup of suspected CPA were frequently inconsistent with guidelines exposing to false-negative and false-positive cases. Obstacles to a complete implementation of guidelines must be identified and overcome.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".