06 Genetic evaluation and screening for osteogenesis imperfecta in the setting of suspected maltreatment fractures in children aged 0-5 years: a retrospective study
Bibliographic record
Abstract
Abstract Background Fractures are the second most common type of injury due to child maltreatment. Their evaluation must include consideration of osteogenesis imperfecta (OI). There are no clear recommendations to guide clinicians on the need to perform genetic testing for OI in children with suspected non-accidental fracture. When performed, genetic testing may identify « variants of unknown significance» (VUS). Objectives Objectives were to describe the characteristics of patients evaluated for fractures at a child maltreatment paediatrics clinic (CMPC) with emphasis on the evaluation for possible OI features, to determine the nature of the genetic tests when performed and the proportion of VUS results. We aimed to assess the impact of these VUS on the final diagnosis and management of our patients. Design/Methods This descriptive retrospective cohort study included children between 0 and 5 years old who were evaluated by the CMPC for at least one fracture between 2016 and 2022 in a Canadian paediatric tertiary care center. Data collection included personal and familial history along with the presence of other physical injuries and possibles physical signs of OI, bone health specialist's consultations, genetic tests and final CMPC conclusions. Results 126 children were included and 45 underwent genetic testing for OI. Of those, 57,8 % had a dominant and recessive OI comprehensive panel or a bone fragility and fracture panel. In total, 11 different genetic panels were used. Out of 45 patients, 15 (33%) had a VUS and 1 patient was diagnosed with OI. VUS results led to genetic consultation or follow up appointment for 10 patients and testing of the parents for 6 patients. In no cases did the VUS affected the final CMPC opinion Conclusion Although VUS were found in a third of patients, leading to more investigations in most cases, the clinicians can be reassured that it is possible to scientifically interpret the result of those VUS and that those VUS did not change the conclusion of the clinician when non-accidental trauma was suspected. In our center, many different genetic tests were used, without a consensus, further supporting the need for clearer guidelines in those situations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".