E-Health & Innovation to Overcome Barriers in Neuromuscular Diseases. Report from the 1st eNMD Congress: Nice, France, March 22-23, 2019
Bibliographic record
Abstract
By definition, neuromuscular diseases are rare and fluctuating in terms of symptoms; patients are often lately diagnosed, do not have enough information to understand their condition and be proactive in their management. Usually, insufficient resources or services are available, leading to patients' social burden. From a medical perspective, the rarity of such diseases leads to the unfamiliarity of the medical staff and caregiver and an absence of consensus in disease assessment, treatment, and management. Innovations have to be developed in response to patients' and physicians' unmet needs.It is vital to improve several aspects of patients' quality of life with a better comprehension of their disease, simplify their management and follow-up, help their caregiver, and reduce the social and economic burden for living with a rare debilitating disease. Database construction regrouping patients' data and symptoms according to specific country registration on data privacy will be critical in establishing a clear consensus on neuromuscular disease treatment.Clinicians also need technological innovations to help them recognize neuromuscular diseases, find the best therapeutic approach based on medical consensus, and tools to follow patients' states regularly. Diagnosis also has to be improved by implementing automated systems to analyze a considerable amount of data, representing a significant step forward to accelerate the diagnosis and the patients' follow up. Further, the development of new tools able to precisely measure specific outcomes reliably is of the matter of importance in clinical trials to assess the efficacy of a newly developed compound.In this context, creation of an expert community is essential to communicate and share ideas. To this end, 97 clinicians, healthcare professionals, researchers, and representatives of private companies from 9 different countries met to discuss the new perspective and challenges to develop and implement innovative tools in the field of neuromuscular diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".