Source document verification in the Mucopolysaccharidosis Type I Registry
Bibliographic record
Abstract
PURPOSE: The Mucopolysaccharidosis Type I (MPS I) Registry is an international observational database that tracks the natural history and the outcomes of patients with MPS I. The Registry was a regulatory requirement following the approval of laronidase enzyme replacement therapy for MPS I in 2003. All data are collected voluntarily after informed consent from the patient or family. Data are checked through queries, monthly reviews, and electronic audits to identify missing, inconsistent, or invalid data. This analysis sought to determine overall data accuracy in the Registry through source document verification (SDV). METHODS: Two phases of SDV were performed. In each phase, Registry data were compared against source documents at sites in Europe, Latin America, and North America. Three patients were randomly selected for SDV at each of the selected sites among all patients enrolled ≥18 months and ever receiving laronidase. Key parameters central to MPS I and its treatment were examined from the baseline and the last available assessments. RESULTS: Results indicate an overall source-to-database error rate in the MPS I Registry of 2.7% (47 discrepancies out of 1715 items; 95% confidence interval [2.2%, 3.5%]) in Phase 1 and 3.7% (64 discrepancies out of 1732 items; 95% confidence interval [2.9%, 4.7%]) in Phase 2. No systematic errors were found. CONCLUSIONS: The overall error rates in both phases of SDV demonstrate acceptable data accuracy in the MPS I Registry within the data fields that were assessed. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.190 | 0.446 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".