Evidence of nerve hypertrophy in patients with inclusion body myositis on lower limb MRI
Bibliographic record
Abstract
Abstract Introduction/aims Inclusion body myositis (IBM) is a myopathic condition but in some patients has been associated with an axonal length‐dependent polyneuropathy. In this study, we quantified the cross‐sectional area of the sciatic and tibial nerves in patients with IBM comparing with Charcot–Marie–Tooth disease type 1A (CMT1A) and healthy controls using magnetic resonance neurography (MRN). Methods MRN of the sciatic and tibial nerves was performed at 3T using MPRAGE and Dixon acquisitions. Nerve cross‐sectional area (CSA) was measured at the mid‐thigh and upper third calf regions by an observer blinded to the diagnosis. Correlations were performed between these measurements and clinical data. Results A total of 20 patients with IBM, 20 CMT1A and 29 healthy controls (age‐ and sex‐matched) were studied. Sciatic nerve CSA was significantly enlarged in patients with IBM and CMT1A compared to controls (sciatic nerve mean CSA 62.3 ± 22.9 mm2 (IBM) vs. 35.5 ± 9.9 mm2 (controls), p < 0.001; and 96.9 ± 35.5 mm2 (CMT1A) vs. 35.5 ± 9.9 mm2 (controls); p < 0.001). Tibial nerve CSA was also enlarged in IBM and CMT1 patients compared to controls. Discussion MRN reveals significant hypertrophy of the sciatic and tibial nerves in patients with IBM and CMT1A compared to controls. Further studies are needed to correlate with neurophysiological measures and assess whether this finding is useful diagnostically.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".