Chondrodysplasia with multiple dislocations: comprehensive study of a series of 30 cases
Bibliographic record
Abstract
The group of chondrodysplasia with multiple dislocations includes several entities, characterized by short stature, dislocation of large joints, hand and/or vertebral anomalies. Other features, such as epiphyseal or metaphyseal changes, cleft palate, intellectual disability are also often part of the phenotype. In addition, several conditions with overlapping features are related to this group and broaden the spectrum. The majority of these disorders have been linked to pathogenic variants in genes encoding proteins implicated in the synthesis or sulfation of proteoglycans (PG). In a series of 30 patients with multiple dislocations, we have performed exome sequencing and subsequent targeted analysis of 15 genes, implicated in chondrodysplasia with multiple dislocations, and related conditions. We have identified causative pathogenic variants in 60% of patients (18/30); when a clinical diagnosis was suspected, this was molecularly confirmed in 53% of cases. Forty percent of patients remain without molecular etiology. Pathogenic variants in genes implicated in PG synthesis are of major importance in chondrodysplasia with multiple dislocations and related conditions. The combination of hand features, growth failure severity, radiological aspects of long bones and of vertebrae allowed discrimination among the different conditions. We propose key diagnostic clues to the clinician.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".