Editorial: Biomarkers and Clinical Indicators in Motor Neuron Disease
Bibliographic record
Abstract
Motor neuron diseases (MNDs) encompass a range of progressive neurodegenerative conditions with heterogeneous clinical presentations, disability profiles, prognosis, and age of onset.The umbrella term MND typically includes amyotrophic lateral sclerosis (ALS) (1), primary lateral sclerosis (PLS) (2, 3), progressive muscular atrophy (PMA), hereditary spastic paraplegia (HSP), spinal muscular atrophy (SMA) (4, 5), spinal and bulbar muscular atrophy (SBMA) (6), and rare conditions such as monomelic amyotrophy (MMA), juvenile muscular atrophy of distal upper extremity (JMADUE) (7), Mill's disease (8), ALS-FTD complex (9), and progressive bulbar palsy (PBP) (10).Despite the diversity of the clinical phenotypes, MNDs share a number of fundamental traits such as a long presymptomatic phase (11), insidious onset (12), considerable diagnostic challenges (13, 14), relatively low incidence (15, 16), extra-motor (Christidi et al.; Christidi et al.) and extra-neurological manifestations (6), relentless progression (17, 18), multidisciplinary care needs (19,20), and lack of effective disease modifying therapies.These core similarities justify the discussion of various MNDs in a dedicated collection of articles and offer the opportunity to exchange research ideas between centres focusing on specific MNDs.There are other shared challenges across the MND spectrum, which are particularly relevant for therapy development, chief of which is the lack of validated biomarkers to serve as outcome measures in clinical trials.Pharmaceutical trials in MNDs mostly rely on functional rating scales and survival instead of objective, observer-independent markers which reflect on the underlying pathology of the condition (1,21,22).
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 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".