PATIENT-REPORTED OUTCOMES IN RARE LYSOSOMAL STORAGE DISEASES: KEY INFORMANT INTERVIEWS AND A SYSTEMATIC REVIEW PROTOCOL
Bibliographic record
Abstract
OBJECTIVES: To investigate the use, challenges and opportunities associated with using patient-reported outcomes (PROs) in studies with patients with rare lysosomal storage diseases (LSDs), we conducted interviews with researchers and health technology assessment (HTA) experts, and developed the methods for a systematic review of the literature. The purpose of the review is to identify the psychometrically sound generic and disease-specific PROs used in studies with patients with five LSDs of interest: Fabry, Gaucher (Type I), Niemann-Pick (Type B) and Pompe diseases, and mucopolysaccharidosis (Types I and II). METHODS: Researchers and HTA experts who responded to an email invitation participated in a telephone interview. We used qualitative content analysis to analyze the anonymized transcripts. We conducted a comprehensive literature search for studies that used PROs to investigate burden of disease or to assess the impact of interventions across the five LSDs of interest. RESULTS: Interviews with seven researchers and six HTA experts representing eight countries revealed five themes. These were: (i) the importance of using psychometrically sound PROs in studies with rare diseases, (ii) the paucity of disease-specific PROs, (iii) the importance of having PRO data for economic analyses, (iv) practical and psychometric limitations of existing PROs, and (v) suggestions for new PROs. The systematic review has been completed. CONCLUSIONS: The interviews highlight current challenges and opportunities experienced by researchers and HTA experts involved in work with rare LSDs. The ongoing systematic review will highlight the experience, opportunities, and limitations of PROs in LSDs and provide suggestions for future research.
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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 | 0.143 | 0.119 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".