Muscle ultrasonography in detecting fasciculations: A noninvasive diagnostic tool for amyotrophic lateral sclerosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Muscle ultrasound (MUS) is an emerging noninvasive tool to identify fasciculations in amyotrophic lateral sclerosis (ALS). We assessed the utility of MUS in detecting fasciculations in suspected ALS patients. METHODS: Thirty-three patients (25 men) with possible (n = 7), probable (n = 12), or definite ALS according to Awaji criteria were studied. Electromyography was done in biceps brachii, quadriceps, and thoracic paraspinal muscles and MUS in biceps, triceps, deltoid, abductor-digiti-minimi, quadriceps, hamstrings, tibialis anterior, thoracic paraspinal, and tongue muscles. RESULTS: The age at onset and illness duration was 49.73 ± 12.7 years and 13.57 ± 9.7 months, respectively. Limb-onset = 24 patients (72.7%) and bulbar-onset = 9 (27.3%). Totally 561 muscles were examined by MUS. Fasciculations were detected in 84.3% of muscles, 98.4% with and 73% without clinical fasciculations (p < 0.001). Fasciculation detection rate (FDR) by MUS was significantly higher in muscles with wasting (95.6%) than without wasting (77.6%, p < 0.001). Compared with EMG, FDR was significantly higher with MUS in quadriceps (81.8% vs. 51.5%, p = 0.002) and thoracic paraspinal muscles (75.8% vs. 42.4%, p = 0.013). The proportion of patients with definite ALS increased from 42% by clinical examination to 70% after combining EMG and MUS findings. CONCLUSIONS: MUS is more sensitive in detecting fasciculations than electromyography (EMG) and provides a safer, faster, painless, and noninvasive alternative to EMG in detecting fasciculations in ALS.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".