Improved Criteria for the Classification of Titin Variants in Inherited Skeletal Myopathies
Bibliographic record
Abstract
BACKGROUND: Extensive genetic screening results in the identification of thousands of rare variants that are difficult to interpret. Because of its sheer size, rare variants in the titin gene (TTN) are detected frequently in any individual. Unambiguous interpretation of molecular findings is almost impossible in many patients with myopathies or cardiomyopathies. OBJECTIVE: To refine the current classification framework for TTN-associated skeletal muscle disorders and standardize the interpretation of TTN variants. METHODS: We used the guidelines issued by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) to re-analyze TTN genetic findings from our patient cohort. RESULTS: We identified in the classification guidelines three rules that are not applicable to titin-related skeletal muscle disorders; six rules that require disease-/gene-specific adjustments and four rules requiring quantitative thresholds for a proper use. In three cases, the rule strength need to be modified. CONCLUSIONS: We suggest adjustments are made to the guidelines. We provide frequency thresholds to facilitate filtering of candidate causative variants and guidance for the use and interpretation of functional data and co-segregation evidence. We expect that the variant classification framework for TTN-related skeletal muscle disorders will be further improved along with a better understanding of these diseases.
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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 | 0.055 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".