Clinical trial design in haemophilia
Bibliographic record
Abstract
Progress in the evidence-based care of haemophilia A and B worldwide has been historically challenged by the dearth of evaluable outcome data, including but not limited to the safety and effectiveness of therapeutic interventions. These challenges are partially rooted in the inherent difficulty of conducting prospective clinical trials and observational studies with statistically meaningful endpoints in a rare disease such as haemophilia. Despite the logistical barriers, the need for outcome data has never been more critical than in this time of expansive therapeutic advance tempered by the shrinking economic capacity to fund the rapidly increasing cost of treatment. Given that systematic analyses of published literature have been largely unsuccessful in compensating for the lack of rigorous and purposeful data collection, new approaches to clinical study design and statistical modelling are urgently needed. However, even as these are considered, the lack of broadly accepted and well-defined clinical outcome endpoints poses an additional barrier to progress. The three presentations encompassed by this paper highlight the timely need for quality data from the perspectives of the clinicians, regulatory agencies and health care funders, and describe the ongoing coordinated efforts by the international haemophilia community to further understand and dismantle the barriers to harmonized and standardized data collection on a global scale using well-defined clinical outcome endpoints.
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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 | 0.224 | 0.367 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".