Qualitative Review of Quantitative EMG Results Can Improve Needle EDX Studies (P6.261)
Bibliographic record
Abstract
Needle electrodiagnostic (EDX) examination is gold standard for diagnosing neuromuscular conditions. Motor unit potentials (MUPs) are evaluated qualitatively or semi-quantitatively for motor unit size, morphology and recruitment to determine the presence or absence of disease. Decomposition based quantitative electromyography (DQEMG) is a computer based algorithm which decomposes electromyographic signals acquired during EDX examination into composite MUP trains, that can be individually quantitatively analyzed and collectively contribute to the characterization of muscle. Methods: EDX examination was performed during low level muscle contraction at four different needle positions in all the tested muscles and muscles were classified qualitatively as normal, myopathic or neurogenic. In addition, at each needle position, the level of contraction was increased to 40-60MUPs per second and 10 seconds of needle EMG data was acquired. MUP trains were isolated and analyzed using DQEMG algorithm and a separate qualitative assessment of the quantitative EMG results was performed, based on MUP size, dispersion and stability. Muscles were characterized as normal, myopathic or neurogenic. The qualitative assessment of each muscle based on routine examination was compared with the qualitative assessment using results provided by DQEMG algorithm. Results: 206 muscles were sampled from 45 patients. Routine assessment agreed with the qualitative assessment from DQEMG in 181/ 206 muscles (87.9[percnt]). 9 muscles categorized normal using routine assessment were characterized myopathic in 4 muscles (1.9[percnt]) and neurogenic in 5 muscles (2.4[percnt]) based on DQEMG results. The remaining 16 muscles (7.8[percnt]) characterized mildly neurogenic using routine examination were characterized normal based on DQEMG results, as the MUP morphology and stability were thought to be age appropriate. Conclusion: Qualitative assessment based on DQEMG results provides more accurate and additional quantitative information than routine EDX and may aid in clarifying the diagnosis in some of the diagnostic challenging patients. It can be completed in a reasonable time frame.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".