Using jitter analysis with concentric needle electrodes to assess disease status and treatment responses in myasthenia gravis
Bibliographic record
Abstract
Objective: This study assesses the utility of jitter analysis with concentric needles to evaluate disease severity in myasthenia gravis (MG), correlate changes in jitter with clinical status as well as identify reasons for any discordance. Methods: We performed a retrospective chart review of 82 MG patients and extracted data on demographics, MG subtype, antibody status, clinical scales, electrophysiology, and interventions at baseline and follow-up. Results: Baseline MGII scores correlated with jitter (r = 0.25, p = 0.024) and abnormal pairs (r = 0.24, p = 0.03). After 28 months, MGII scores correlated with jitter (r = 0.31, p = 0.006), abnormal pairs (r = 0.29, p = 0.009), and pairs with blocks (r = 0.35, p = 0.001). Changes in MGII scores correlated with changes in jitter (r = 0.35, p = 0.002), abnormal pairs (r = 0.27, p = 0.014), and pairs with blocks (r = 0.36, p = 0.001). Conclusions: Concentric needle jitter analysis may have the potential to evaluate baseline and sequential disease severity in MG. Significance: This study highlights the potential for improved MG patient care through precise assessment and management using concentric needle jitter analysis to improve the accuracy of MG diagnosis and monitoring of disease activity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".