Challenges and key lessons from the design and implementation of an international haemophilia registry supported by a pharmaceutical company
Bibliographic record
Abstract
INTRODUCTION: Real-world data are lacking regarding the relationship between prospectively collected patient-reported outcomes (PROs), clinical outcomes and treatment in people with haemophilia (PWH). The Expanding Communications on Hemophilia A Outcomes (ECHO) registry was designed to address this data gap, but a range of difficulties led to early study closure. AIM: To describe the challenges faced and lessons learned from implementing a multinational haemophilia registry. METHODS: The Expanding Communications on Hemophilia A Outcomes was planned as a five-year observational cohort study to collect data from 2000 patients in nine countries. Based on direct observations, feedback from patients enrolled in ECHO, challenges of the study design and input from study-sponsor representatives, the ECHO Steering Committee systematically identified the challenges faced and developed recommendations for overcoming or avoiding them in future studies. RESULTS: The study closed after two years because few countries were activated and patient recruitment was low. This was related to multiple challenges including delayed implementation, stringent pharmacovigilance requirements, objections of investigators and patients to the burden of multiple PROs, data collection issues, lack of resources at study sites, little engagement of patients and competing clinical trials, which further limited recruitment. At study closure, 269 patients had been enrolled in four of nine participating countries. CONCLUSIONS: Researchers planning studies similar to ECHO may want to consider the barriers identified in this global registry of PWH and suggestions to mitigate these limitations, such as greater patient involvement in design and analysis, clearer assessment and understanding of local infrastructure and potential changes to the administration of the study.
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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 | 0.667 | 0.606 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.013 | 0.022 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".