The Muscle-Bone Relationship in X-Linked Hypophosphatemic Rickets
Bibliographic record
Abstract
CONTEXT: We recently found that patients with X-linked hypophosphatemic rickets (XLH) have a muscle function deficit in the lower extremities. As muscle force and bone mass are usually closely related, we hypothesized that patients with XLH could also have a bone mass deficit in the lower extremities. OBJECTIVE: The study objective was to assess the muscle-bone relationship in the lower extremities of patients with XLH. SETTING: The study was carried out in the outpatients department of a pediatric orthopedic hospital. PATIENTS AND OTHER PARTICIPANTS: Thirty individuals with XLH (6 to 60 y; 9 male patients) and 30 age- and gender-matched controls participated. MAIN OUTCOME MEASURES: Calf muscle size and density as well as tibia bone mass and geometry were assessed by peripheral quantitative computed tomography. Muscle function was evaluated as peak force in the multiple 2-legged hopping test. RESULTS: Muscle force was significantly lower in XLH patients than in controls but muscle cross-sectional area did not differ (after adjustment for tibia length). External bone size, expressed as total bone cross-sectional area, was higher in the XLH group than in controls. The XLH cohort also had statistically significantly higher bone mineral content. CONCLUSIONS: Patients with XLH have increased bone mass and size at the distal tibia despite muscle function deficits.
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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 | 0.000 |
| 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) | 0.001 | 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".