P.056 Survey of Canadian myotonic dystrophy patients’ access to computer technology
Bibliographic record
Abstract
Background: Myotonic dystrophy is an autosomal dominant condition affecting distal hand strength, energy and cognition. There is a neuromuscular patient portal under development that has the potential to give voices and resource access to patients, regardless of location via the internet and social media. The current state of access to technology and underlying factors affecting use and interest were explored. Methods: Surveys were mailed to 156 participants with myotonic dystrophy type 1 (DM1) through the Canadian Neuromuscular Disease Registry. The survey questions touched on demographics, technology use, internet use, and interest in the portal. Results: Seventy-two participants (43 female) responded so far and 50% were younger than 46 years. Most (62/72) used the internet and 94% of participants had access to an internet-connected device. Almost half of the responders (34/72) used social media. The complexity and cost of technology were commonly cited reasons not to use technology. The majority of responders (79%) were interested in a myotonic dystrophy patient portal. Conclusions: DM1 patients have access to and use technology such as computers and mobile phones. They expressed interest in a portal that would provide them with access to relevant information such as guidelines, self-management modules, educational videos, and support groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".