Clinical and Genetic Knowledge and Attitudes of Patients with Myotonic Dystrophy Type 1
Bibliographic record
Abstract
AIMS: The goal was to assess clinical and genetic knowledge and attitudes in patients affected by myotonic dystrophy type 1 (DM1). METHODS: Two hundred patients with molecular confirmation of the diagnosis of DM1 completed a multi-choice questionnaire. DM1 patients' knowledge and views were compared to clinically normal DM1 noncarriers (n = 264) and controls (n = 1,474). RESULTS: Knowledge of the DM1 mode of inheritance was better in noncarriers than in patients (p < 0.001). Noncarriers were more aware than DM1 patients of the common clinical characteristics of DM1 such as limitations in physical activities and problems related to employment, schooling, activities of daily living, parenthood, peer relationships, and personality (p < 0.001). Compared to controls, DM1 patients felt less informed about the availability of clinical genetic services (p < 0.05) and new genetic technologies (p < 0.001). Among patients, logistic regression revealed that each additional year of education (p < 0.05) and each additional 100 CTG repeats (p < 0.01), respectively, increased and decreased the odds of knowing the DM1 mode of inheritance by about 23% and 18% respectively, independently of age, age at onset of symptoms, gender, severity of muscular impairment, and intellectual quotient. CONCLUSIONS: DM1 patients' genetic knowledge is significantly dependent of the level of education and the number of CTG repeats. Healthcare providers should be aware of this situation in order to adjust counselling and education accordingly.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".