The First Decade of <i>Journal of Neuromuscular Diseases</i> : Supporting and Advancing the Rapidly Evolving Field of Translational Research
Bibliographic record
Abstract
This issue marks the 10th anniversary of the Journal of Neuromuscular Diseases (JND) that we -as Co-Editors-in-Chief -helped inaugurate in 2014.Ten years ago, we were encouraged both by colleagues and by market research of the publisher IOS Press that a new journal focused on translational research in neuromuscular disorders would be welcome by the community and fill a space between the more clinically oriented and the more basic science publications.Looking back we feel we have made the right call to accept this invitation, as the last years brought major scientific progress towards translation in the neuromuscular diseases, including next generation exome, genome and RNA sequencing for gene discovery and better and more precise diagnostic yield, an increasing importance and number of both preclinical and clinical studies for better therapies, often based on genetic mechanisms and finally the marketing approval of several disease-modifying genetic treatments in spinal muscular atrophy, Duchenne muscular dystrophy and amyloid neuropathy among others.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
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".