Hip Dysplasia in Charcot–Marie–Tooth Disease: Insights From a Large Cohort of Children and Adolescents
Bibliographic record
Abstract
BACKGROUND AND AIMS: Despite the known association of hip dysplasia and Charcot Marie Tooth disease (CMT), evidence is limited regarding its exact prevalence. Available studies pre-date genetic confirmation of CMT subtypes and current hip reconstruction surgical options. This study examined the prevalence of hip dysplasia in CMT in a tertiary neuromuscular center. METHODS: This was a retrospective study of children with CMT who had at least one pelvic radiograph between 2000 and 2020. Reimer's migration percentage, acetabular index and lateral center edge angle were used to identify hip dysplasia. RESULTS: A total of 178 children were included with a median age of 6.4 (IQR 3.4-11.3) years at CMT diagnosis. First pelvic radiographs were performed at a median age of 8.0 (IQR 4.6-12.2) years and 64 (35.8%) had hip dysplasia, of which 20 normalized over time. Repeat radiographs were done in 96/178 children (53.9%), and six children with originally normal radiographs developed later radiographic hip dysplasia. At the time of last follow up, 50/178 children (28.1%) had hip dysplasia and 17/178 children (9.6%) required surgical intervention. The frequency of hip dysplasia in specific CMT subtypes was: 28/100 in CMT1A, 5/7 in Dejerine-Sottas disease, 3/10 in CMT2A, and 4/4 in TRPV4-related CMT. INTERPRETATION: The prevalence of hip dysplasia in children with CMT in this cohort was estimated to be between 9.6% and 28.1%. Serial imaging is important to monitor outcomes into adulthood. Specific CMT subtypes were more likely to be associated with hip dysplasia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".