Evaluating Patients With Mucopolysaccharidosis Type <scp>III</scp> : A Scoping Review on Diagnostic and Follow‐Up Approaches
Bibliographic record
Abstract
BACKGROUND: Mucopolysaccharidosis type III (MPS III) is a rare lysosomal storage disease with systemic complications. This scoping review aimed to synthesise evidence regarding methods to diagnose and monitor MPS III. METHODS: We searched 10 databases for English and Spanish citations published from 2017 to 2022. Our study focused on human-based research that reported methods to diagnose and monitor MPS III, including experimental, quasi-experimental, observational studies, reviews, and guidelines. We followed PRISMA-ScR guidelines for screening and data extraction. Descriptive statistics and qualitative synthesis were used for analysis. RESULTS: We included 35 eligible articles. Most studies were conducted in Europe and Central Asia (17/35, 48.6%) and constituted case reports (19/37, 51.4%). Clinical symptoms were reported in 30 articles and predominantly pertained to the nervous system (25/30, 83.3%), including intellectual disability (19/30, 63.3%), movement incoordination (17/30, 56.6%), and behavioural issues (16/30, 53.3%). Diagnostic methods (23/35, 65.7%) featured genetic testing and biochemical assays, including GAG measurement in urine. Brain MRI was the most recorded imaging study (11/20, 55.0%), while electrographic studies (10/35, 28.5%) encompassed electrocardiogram (5/10, 50%), electroencephalogram, and audiometry (3/10, 30% each). Scales and questionnaires (8/35, 22.8%) were reported, with the Bayley Scales of Infant Development being the most described (4/8, 50%). CONCLUSION: Our study comprehensively overviews the contributions of diagnostic and monitoring methods for MPS III. Our findings can guide clinicians in providing evidence-based care for this rare disease.
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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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".