Algorithm for jitter measurement in neuromuscular junction disease
Bibliographic record
Abstract
The objective of this work was to extend the evaluation of a recently proposed method for estimating neuromuscular jitter within motor unit potential (MUP) trains extracted from muscles suffering neuromuscular junction disease. The method detects, within the MUP duration, "single-fiber" intervals that have likely been produced by single muscle fibers. Jitter is then estimated between pairs of these "single-fiber" intervals using an algorithm which incorporates the traditional mean consecutive difference (MCD) parameter. Electromyographic (EMG) recordings from facial muscles of 15 patients with symptoms related to myasthenia gravis were obtained. MUP trains were extracted using DQEMG software and manual jitter measures were obtained using an ad-hoc graphical interface, which emulates single fiber EMG protocols. Automatic measures for two different values of an internal threshold parameter were obtained and compared to manual measures. 5 %, 25 %, 75 % and 95 % percentiles for the differences between the automatic and manual jitter measurements were [-3.74, -1.47, 1.24, 3.47 μs] and [-6.45, -2.07, 1.65, 7.16 μs], for the two threshold values, respectively. Therefore, very small statistical and clinical differences were found between the automatic and manual jitter measures, supporting the method as an accurate tool for jitter assessment or as a guiding aid for manual procedures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".