Management of rare and undiagnosed diseases: insights from researchers and healthcare professionals in Türkiye
Bibliographic record
Abstract
Introduction: Diagnosis, treatment and management of rare diseases (RD) pose unique challenges due to their complex nature, significantly impacting the daily experiences of researchers and healthcare professionals working in this field. Despite increasing awareness and progress in the field of RD worldwide in recent years, a significant gap remains in our understanding of the specific barriers that these professionals face in their work with RD. This study provides a detailed survey analysis that sheds light on the challenges that researchers and healthcare professionals face in diagnosing, treating, managing and conducting research on RD. Methods: We developed a national online survey with three RD stakeholder groups (Researchers, Healthcare professionals and researcher-healthcare professionals) to identify the main challenges and needs in Türkiye for the diagnosis, treatment and follow-up processes of rare and undiagnosed diseases. Results: The survey was completed by 363 participants, revealing that participants face key challenges such as the need to refer patients to specialized centers, financial burdens, limited access to necessary tests, inadequate support for rare disease research and a lack of interdisciplinary collaboration. Participants also noted that RD are inherently difficult to conduct research on with small cohorts. Survey results also suggest a number of policy improvements to accelerate research on RD: increased funding, establishment of robust surveillance systems, and development of comprehensive national action plans and guidelines on RD. Discussion: To the best of our knowledge, this is the first study to be conducted in Türkiye. This study contributes to the understanding of the needs of professionals in rare disease research and highlights the urgent need for system improvements to support them.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".