Exploring the Role of Telemedicine in Duchenne Muscular Dystrophy: Benefits and Challenges
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is the most frequent, progressive disease caused by a genetic defect that leads to the production of a nonfunctional form of dystrophin, thereby causing premature death. Ways to improve, adapt, and facilitate the care of people with DMD are still being explored. This viewpoint, developed by an accredited Duchenne center, aims to present current telemedicine options specifically tailored for patients with DMD and to discuss the advantages and limitations of these approaches across various health care domains. As one of the first centers in Poland to implement such an approach, the accredited Duchenne center provides targeted home-based care by using digital platforms and telemedicine tools. Additionally, we explore the potential of telemedicine to support different types of remote communication, including provider-to-provider, between patient/caregiver and provider, and between patient/caregiver and patient/caregiver interactions. This model has the potential to significantly enhance access to specialized care and improve the continuity and quality of life for those living with DMD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".