An analysis of bone marrow burden scores in a retrospective analysis of adult patients with type 1 Gaucher disease
Bibliographic record
Abstract
BackgroundIn Gaucher disease type 1 (GD1), excess glucocerebroside, can accumulate within the bone marrow, leading to pain, infarcts, reduced density, and severe crises. The bone marrow burden (BMB) score estimates infiltration by evaluating the femora and lumbar spine. BMB is regarded as the most reliable semi-quantitative scoring system, but data on its relationship with other clinical parameters in GD1 is limited.MethodsWe conducted a retrospective chart analysis of 128 adults with GD1 followed in the Ontario and Québec, Canada, and correlated BMB scores with disease severity (DS-3 score), genotype, spleen and liver status, bone mineral density, and quality of life measures (SF-36).ResultsSmall, but significant correlations were observed between BMB scores and DS-3, (r=0.44), hemoglobin (r=–0.22), platelets (r=–0.26), liver volume (r=0.27), and spleen volume (r=0.28), but not between BMB and patient-reported pain or physical functioning. Although BMB scores were lower in treated patients (mean difference=1.07), there was no time-based interaction, and the mean change of 1 point was not considered clinically significant.ConclusionsAdditional studies may be required to fully understand the utility of BMB scores in GD1 patients with established disease. Our results suggest that less frequent monitoring could be possible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".