Towards early diagnosis of amyotrophic lateral sclerosis using near fibre EMG
Bibliographic record
Abstract
OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is characterized by progressive loss of motor neurons and diagnosis is usually delayed several months. The continuous denervation and reinnervation associated with ALS are manifest in EMG signals as changes in motor unit potential (MUP) size, temporal dispersion and instability. Near Fibre EMG is a novel method to assess early changes in MUP temporal dispersion and instability using routinely recorded EMG signals in a semi-automated manner. METHODS: Near Fibre EMG values from 2318 MUs, retrospectively sampled at the time of ALS diagnosis, from 96 muscles of 15 patients were compared with values from 3954 MUs sampled from 109 muscles of 84 reference subjects. RESULTS: 30.1% and 46.1% of ALS MUs had MUPs with increased complexity or instability, respectively, and 17.4% had both. The potential importance and heightened sensitivity of NFEMG was highlighted when analyzing normal-sized motor units; as many as 24% of the normal-sized MUPs actually had significant instability, while 14% had increased complexity, and 7.4% had both. CONCLUSIONS: Near Fibre EMG can characterize motor unit electrophysiological status and hence help quantify the degree, and course of denervation and reinnervation. SIGNIFICANCE: Near-Fiber EMG offers the potential to facilitate earlier ALS diagnosis, which, as promising therapies become available, can be consequential.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".