ELECTROMYOGRAPHY
Bibliographic record
Abstract
Purpose: The purpose of this study was to evaluate if surface electromyography (sEMG) of the Vastus Lateralis (VL) muscle during maximal incremental ergocycle test (ET) and isometric contraction (MVC) could be used to detect peripheral muscle abnormalities in 14 CHF (Ejection fraction = 25.93 ± 7.02%), 25 COPD (FEV1 = 40.52 ± 21.24% pred.), and 16 age matched healthy subjects. Method: Subjects executed isometric contractions of the quadriceps at various % of MVC while sEMG was recorded over the belly of the VL at the location of the future biopsy. The next day, the subjects performed an ET while sEMG was recorded over the freshly biopsied VL muscle. Median Frequency (MDF) and integrated electromyography (iEMG) were subsequently analysed using Acknowledge v.3.2 software. A discriminant analysis (Quadratic model) divided the population in 39 subjects with muscle abnormalities and 16 subjects without evidence of muscle abnormalities. The discriminatory variables established from healthy subjects were % and surface area of type I muscle fibres, the whole quadriceps muscle surface area from tomodensitometry imaging and the ratio of the activity the enzyme phosphofructokinase over the 3-hydroxyacyl CoA dehydrogenase (PFK/HADH). RESULTS: ROC curves analysies revealed a sensitivity of 81.3% and a specificity of 58.3% using ΔMDF/wattmax values obtained during the ET (Max MDF-Min MDF expressed per unit of wattmax achieved). Results were slightly better using ΔiEMG. (Sensitivity: 85.7% & Specificity: 66.7%). Furthermore, at 80% of MVC, the MDF expresses a sensitivity of 71.4% and a specificity of 83.3% while iEMG showed a sensitivity of 57.1% and a specificity of 85.7%. CONCLUSION: Since previous studies from De Luca et al. (1985), Moritani et al. (1998), and Gerdle et al. (2000) have shown that the sEMG spectrum is undoubtedly influenced by metabolites, fibre typing, and the state of fatigue of the muscle, we conclude that these data show a great potential for sEMG as a non invasive tool to identify patients with muscle abnormalities. Sponsored by Bayer. inc.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.040 |
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".