Relationships Between Nerve Ultrasound Findings and Clinical, Genetic, Laboratory and Electrophysiological Findings in Patients With Chronic Idiopathic Axonal Polyneuropathy
Bibliographic record
Abstract
INTRODUCTION/AIMS: Chronic idiopathic axonal polyneuropathy (CIAP) is an increasingly recognized, frequent and often disabling disease with unknown etiology. We explored the utility of nerve ultrasound in the investigation of patients with predominantly sensory CIAP, by comparing ultrasound with clinical, genetic, laboratory and electrophysiological findings. METHODS: Thirty-nine patients with a predominantly sensory CIAP were included. The averaged measurements of the ultrasound cross-sectional areas (CSAs) of median and ulnar nerves at mid-forearm and mid-humerus (Mean UL CSAs) for each patient were compared with demographics, neuropathy scores (INCAT Sensory Sumscore (ISS) and Toronto Clinical Neuropathy Score), blood tests, repeat nerve conduction studies and, given the RFC1 association with late-onset sensory neuropathy, genetic RFC1 testing. RESULTS: ). Ultrasound abnormalities were associated with two distinct phenotypes: Most of the six patients with small CSAs had normal (five patients) or only mildly elevated body mass index (BMI), were homozygously RFC1 expansion positive (four) and had a possible sensory neuronopathy based on Camdessanché criteria (five). Most patients with enlarged CSAs were overweight (93%), a majority had obesity (67%) and none was RFC1 positive. DISCUSSION: This exploratory study supports a role for ultrasound in the differential diagnosis of CIAPs: reduced CSAs of upper limb nerves suggest inherited sensory neuronopathy, whereas increased CSAs make inherited sensory neuronopathy unlikely and, in combination with obesity, suggest metabolic neuropathy. Larger confirmatory studies are needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".