Improving genetic testing pathways for transthyretin amyloidosis in France: challenges and strategies
Bibliographic record
Abstract
Transthyretin amyloidosis (ATTR) is a severe and rare disease characterized by the progressive deposition of misfolded transthyretin proteins, causing irreversible organ damage. Transthyretin amyloidosis can present as a hereditary ATTR or acquired wild-type ATTR form. Genetic testing is critical for determining a hereditary predisposition and subsequently initiating appropriate family screening. In France, strict regulations govern genetic testing that aim to protect patients and their families affected by hereditary diseases such as ATTR. However, challenges persist in establishing an effective genetic testing pathway. A multidisciplinary group of French experts convened to discuss the challenges associated with an ATTR genetic diagnosis and to propose improvement strategies. Key challenges include the lack of pathway standardization, communication gaps between healthcare professionals (HCPs) and patients, and difficulties in complying with regulatory requirements. Concerns about patient data safety and outsourced testing quality further complicate matters. Proposed strategies included the development of stakeholder mapping tools for HCPs and patients, educational programs to improve literacy on genetic testing regulations, increase disease awareness among medical geneticists and genetic counselors, and strengthening HCP-patient communication through educational materials. These initiatives aim to streamline the genetic testing pathway, enhance compliance with regulations, and ultimately provide optimal support for patients and families with ATTR.
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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 | 0.077 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".