Impact on bone microarchitecture and failure load in a patient with type I Gaucher disease who switched from Imiglucerase to Eliglustat
Bibliographic record
Abstract
gene. Type 1 GD (GD1) is the most common form and its clinical manifestations include severe hematological, visceral and bone disease. The goal of disease-modifying treatments for GD1 is to reduce substrate storage and hence toxicity from the disease. The two common therapeutic routes for managing GD1 are enzyme replacement therapy (ERT) and substrate reduction therapy (SRT). These therapies have shown to improve hematological and visceral aspects of the disease. However, quantitative investigations into how these therapies may help prevent or improve the progression of bone disease is limited. This case involves a patient diagnosed with GD1 in childhood, who began ERT in young adulthood. Following over 20 years of treatment with ERT, the patient switched to SRT. This case report examined the novel application of high-resolution peripheral quantitative computed tomography (HR-pQCT) in a patient who switched from ERT to SRT. Using bone microarchitecture measurements from HR-pQCT, we applied finite element analysis techniques to calculate the failure load which estimates the resistance to fracture. Over the course of one year following the switch from ERT to SRT therapy, failure load improved in the patient's lower limb. In conclusion, failure load can be computed in the short term in a patient who made a switch from ERT to SRT. Further exploration of failure load in study design to look at interventions that impact bone quality in GD may be considered.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".