Bibliographic record
Abstract
Electrodiagnosis with nerve conduction studies and needle electromyography has been the standard method of diagnosis of neuromuscular conditions for over 80 years. Although ultrasound technology has been in existence for approximately the same time, application of this technology to assessment of the neuromuscular system only began in the 1980s. In the past 2 decades-mainly because of advances in resolution enabled by high frequency transducers and improved image processing-ultrasound has enabled the real-time, morphologic evaluation of focal and diffuse neuropathies, motor neuron diseases, and myopathies. Although methods of nerve and muscle assessment continue to evolve (i.e., Doppler imaging to evaluate intraneural and intramuscular blood flow), nerves have most commonly been evaluated based on their cross-sectional area, which seems to correlate with nerve swelling and pathology, particularly in focal and also in some diffuse neuropathies. Qualitative and more recently quantitative measures of muscle echogenicity have been used in the assessment of myopathies and motor neuron diseases. Collection of normal values in heterogeneous populations, extremes of age and patients with differing anthropometric profiles, has helped develop tables of normal values, thereby allowed ultrasound measurements to be judged against a reference standard, as has previously been established for nerve conduction studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".