GP.3 Measurement of decremental response is repeatable in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Background: Neuromuscular junction transmission impairment has been described in amyotrophic lateral sclerosis (ALS). The degree of repetitive nerve stimulation (RNS) decrement may correlate with disease activity if measurement is repeatable. We determined test-retest correlation of decremental response in patients with ALS. Methods: RNS (3 Hz) was assessed by blinded technician and clinician on median - abductor pollicis brevis (median-APB), ulnar - adductor digiti minimi (ulnar-ADM), and/or accessory - trapezius (accessory-TRAP) nerve-muscle pairs during two evaluations. Repeatability was assessed by measuring the strength of test-retest correlation using the Pearson correlation coefficient (r). Results: 24 patients were included. Decrement was measured for 16 patients on median-APB, 22 on ulnar-ADM, and 24 on accessory-TRAP. Repeated measures of decrement demonstrated strong test-retest correlation for median-APB (r = 0.82, p < 0.001) and accessory-TRAP (r = 0.87, p < 0.001). Correlation was poor in ulnar-ADM (r = −0.01, p = 0.949). Correlation was superior in median-APB muscles with higher Medical Research Council (MRC) grade strength (MRC ≥ 4, r = 0.89, p = 0.001 vs MRC < 4, r = 0.73, p = 0.062). Conclusions: Decremental response in patients with ALS has strong test-retest correlation in median-APB and accessory-TRAP, but not in ulnar-ADM.
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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 | 0.004 | 0.013 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".