E-Health & Innovation to Overcome Barriers in Neuromuscular Diseases. Report from the 3rd eNMD Congress: Pisa, Italy, 29–30 October 2021: Remote Monitoring: New Solutions for New Avenues in Neuromuscular Disorders
Bibliographic record
Abstract
Neuromuscular diseases (NMDs), in their phenotypic heterogeneity, share quite invariably common issues that involve several clinical and socio-economical aspects, needing a deep critical analysis to develop better management strategies. From diagnosis to treatment and follow-up, the development of technological solutions can improve the detection of several critical aspects related to the diseases, addressing both the met and unmet needs of clinicians and patients. Among several aspects of the digital transformation of health and care, this congress expands what has been learned from previous congresses editions on applicability and usefulness of technological solutions in NMDs. In particular the focus on new solutions for remote monitoring provide valuable insights to increase disease-specific knowledge and trigger prompt decision-making. In doing that, several perspectives from different areas of expertise were shared and discussed, pointing out strengths and weaknesses on the current state of the art on topic, suggesting new research lines to advance technology in this specific clinical field.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".