Understanding the Early Presentation of Mucopolysaccharidoses Disorders
Bibliographic record
Abstract
As therapies are developed for rare disorders, challenges of early diagnosis become particularly relevant. This article focuses on clinical recognition of mucopolysaccharidoses (MPS), a group of rare genetic diseases related to abnormalities in lysosomal function. As quality of outcomes with current therapies is impacted by timing of intervention, minimizing time to diagnosis is critical. The objective of this study was to characterize how, when, and to whom patients with MPS first present and develop tools to stimulate earlier recognition of MPS. A tripartite approach was used, including a systematic literature review yielding 194 studies, an online physician survey completed by 209 physicians who described 859 MPS cases, and a global panel of MPS experts who distilled the findings. Red flag signs/symptoms were identified for cardiology, pediatric neurology, otorhinolaryngology, rheumatology, orthopedics, pediatrics, and general medicine and converted into simple, specialty-specific tools intended to facilitate early diagnosis of MPS, enabling improved patient outcomes.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".