Development of a Clinical Algorithm for the Early Diagnosis of Mucopolysaccharidosis III
Bibliographic record
Abstract
Mucopolysaccharidosis III (MPS III) is a rare inherited metabolic disease primarily affecting the central nervous system, leading to developmental and/or speech regression. Early diagnosis of the disease is important to introduce appropriate management measures and to optimize therapeutic outcomes. The diagnosis of MPS III is often significantly delayed due to the rarity of the disease, the more attenuated somatic presentation compared to other MPS types, and the symptom overlap with other developmental disorders. To shorten the time to diagnosis, a list of eight early signs and symptoms was identified through an expert system approach by a global, multidisciplinary working group of 13 specialists with expertise in various aspects of MPS and developmental disorders and three parents of MPS III patients. Coarse facial features and persistent hirsutism or prominent, thick eyebrows were identified as the most important MPS III early signs. The list of eight early MPS III signs and symptoms is the first step towards the development of a clinical algorithm aiming to identify neonates and infants with MPS III before the onset of neurocognitive damage, ultimately shortening the diagnostic journey of MPS III patients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".