Systematic Comparison of Muscle Ultrasound Thickness in Polyneuropathies and Other Neuromuscular Diseases
Bibliographic record
Abstract
BACKGROUND: We have aimed to assess whether muscle thickness ultrasound (US) shows differences between patients with chronic inflammatory demyelinating polyneuropathy (CIDP), chronic axonal polyneuropathy (CAP), and other neuromuscular (NM) diseases compared to controls and to each other. METHODS: We performed a cross-sectional study from September 2021 to June 2022. All subjects underwent quantitative sonographic evaluation of muscle thickness in eight relaxed muscles and four contracted muscles. Differences were assessed using multivariable linear regression, correcting for age and body mass index (BMI). RESULTS: The study cohort consisted of 65 healthy controls, and 95 patients: 31 with CIDP, 34 with CAP, and 30 with other NM diseases. Both relaxed and contracted muscle thickness in all patient groups were lower than in the healthy controls, after controlling for age and body mass index (BMI). Regression confirmed that the differences persisted between patient groups and healthy controls. Differences between patient groups were not apparent. CONCLUSION: The current study shows that muscle ultrasound thickness is not specific in NM disorders, but shows a global reduction in thickness compared with healthy controls after corrections for age and BMI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".