Combination of osteogenesis imperfecta and hypophosphatasia in three children with multiple fractures, low bone mass and severe osteomalacia, a challenge for therapeutic management
Bibliographic record
Abstract
Osteogenesis imperfecta (OI) and hypophosphatasia (HPP) are rare skeletal disorders caused by mutations in the genes encoding collagen type I (COL1A, COL1A2) and tissue-non-specific isoenzyme of alkaline phosphatase (ALPL), respectively. Both conditions result in skeletal deformities and bone fragility although bone tissue abnormalities differ considerably. Children with OI have low bone mass and hypermineralized matrix, whereas HPP children develop rickets and osteomalacia. We report a family, father and three children, affected with growth retardation, low bone mass and recurrent fractures. None of them had rickets, blue sclera or dentinogenesis imperfecta. ALP serum levels were low and genetics revealed in the four probands heterozygous pathogenic mutations in COL1A2 c.838G > A (p.Gly280Ser) and in ALPL c.1333T > C (p.Ser445Pro). After multidisciplinary meeting, a diagnostic transiliac bone biopsy was indicated for each sibling for therapeutic decision. Bone histology and histomorphometry, as compared to reference values of children with OI type I as well as, to a control pediatric patient harboring the same COL1A2 mutation, revealed similarly decreased trabecular bone volume, increased osteocyte lacunae, but additionally severe osteomalacia. Quantitative backscattered electron imaging demonstrated that bone matrix mineralization was not as decreased as expected for osteomalacia. In summary, we observed within each biopsy samples classical features of OI and classical features of HPP. The apparent nearly normal bone mineralization density distribution results presumably from divergent effects of OI and HPP on matrix mineralization. A combination therapy was initiated with ALP enzyme-replacement and one month later with bisphosphonates. The ongoing treatment led to improved skeletal growth, increased BMD and markedly reduced fracture incidence.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".