Spectrum of microarchitectural bone disease in inborn errors of metabolism: a cross-sectional, observational study
Bibliographic record
Abstract
BACKGROUND: Patients diagnosed with inborn errors of metabolism (IBEM) often present with compromised bone health leading to low bone density, bone pain, fractures, and short stature. Dual-energy X-ray absorptiometry (DXA) is the current gold standard for clinical assessment of bone in the general population and has been adopted for monitoring bone density in IBEM patients. However, IBEM patients are at greater risk for scoliosis, short stature and often have orthopedic hardware at standard DXA scan sites, limiting its use in these patients. Furthermore, DXA is limited to measuring areal bone mineral density (BMD), and does not provide information on microarchitecture. METHODS: In this study, microarchitecture was investigated in IBEM patients (n = 101) using a new three-dimensional imaging technology high-resolution peripheral quantitative computed tomography (HR-pQCT) which scans at the distal radius and distal tibia. Volumetric BMD and bone microarchitecture were computed and compared amongst the different IBEMs. For IBEM patients over 16 years-old (n = 67), HR-pQCT reference data was available and Z-scores were calculated. RESULTS: Cortical bone density was significantly lower in IBEMs associated with decreased bone mass when compared to lysosomal storage disorders (LSD) with no primary skeletal pathology at both the radius and tibia. Cortical thickness was also significantly lower in these disorders when compared to LSD with no primary skeletal pathology at the radius. Cortical porosity was significantly greater in hypophosphatasia when compared to all other IBEM subtypes. CONCLUSION: We demonstrated compromised bone microarchitecture in IBEMs where there is primary involvement of the skeleton, as well as IBEMs where skeletal complications are a secondary outcome. In conclusion, our findings suggest HR-pQCT may serve as a valuable tool to monitor skeletal disease in the IBEM population, and provides insight to the greatly varying bone phenotype for this cohort that can be used for clinical monitoring and the assessment of response to therapeutic interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".