The Sensitivity of Quantitative Sonographic Assessment of Muscle Thickness for Amyotrophic Lateral Sclerosis Diagnosis
Bibliographic record
Abstract
PURPOSE: In the current proof-of-concept study, we aimed to examine the sensitivities and specificities of previously reported normal values for muscle ultrasound thickness in amyotrophic lateral sclerosis. METHODS: Muscle ultrasound was performed in 65 healthy control subjects and 91 amyotrophic lateral sclerosis patients using a standardized assessment of eight relaxed muscles and four contracted muscles. Normal values for muscle thickness were determined as values above the 5th percentile stratified by age and gender using the weighted average method. Sensitivity for amyotrophic lateral sclerosis diagnosis was determined for muscles with and without the addition of muscle contraction. RESULTS: Amyotrophic lateral sclerosis patients showed reduced muscle sum thickness both in relaxed and in contracted states compared with control subjects. Muscle ultrasound of muscles with and without contraction showed excellent diagnostic accuracy for differentiating amyotrophic lateral sclerosis patients from control subjects (area under curve = 0.96, sensitivity: 93%-95%, specificity: 84-87). Muscle ultrasound sensitivity was lower within 6 months of symptom onset (83%) compared with longer disease duration (>92%). CONCLUSIONS: Quantitative sonographic assessment of muscle thickness can be complementary in the diagnosis of amyotrophic lateral sclerosis with excellent accuracy for differentiating patients from healthy subjects, and might be useful in other neuromuscular disorders, although additional studies are required.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".