Bridging the gap: Survey highlights challenges and solutions in outreach and identification of people with inherited bleeding disorders
Bibliographic record
Abstract
INTRODUCTION: Inherited bleeding disorders (IBD) are genetic conditions that affect blood clotting, leading to complications such as prolonged or spontaneous bleeding into muscles or joints. Early identification and treatment are crucial to prevent complications and improve outcomes. However, effective patient outreach and identification programs for IBD face significant challenges globally. AIM: This study aimed to identify successful patient outreach initiatives for IBD, barriers encountered during implementation, and approaches used to overcome them. METHODS: The World Federation of Haemophilia (WFH) conducted a survey of its national member organizations and other patient associations, totalling 153 organizations, to identify common strategies, barriers to their implementation, and solutions for outreach and the identification of people with IBD. The survey consisted of both closed-ended and open-ended questions, and the data were analysed using descriptive statistics and thematic analysis. RESULTS: Common challenges included resource and sustainability-related aspects such as financial constraints, limited lab equipment for diagnosis, and inadequate government commitment. Significant barriers also encompassed physical/geographical challenges like difficulty accessing remote areas, and inadequate logistical support and transportation. Seven themes emerged to enhance patient outreach: resource mobilization; awareness-raising and advocacy; knowledge and capacity building; collaboration and partnership; decentralization of services; improved logistical support and infrastructure; utilization of technology and innovation; and financial aid and incentives. CONCLUSION: Multistakeholder collaboration, coupled with secured government commitment, is crucial for improving global outreach, diagnosis rates, and access to care for individuals with IBD. Customized outreach programs should consider regional contexts, financial constraints, and prioritize innovation.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".