Development of a differential suction electrode for improved intravaginal recordings of pelvic floor muscle activity: Reliability and motion artifact assessment
Bibliographic record
Abstract
AIM: The purposes of this study were to compare: (i) the reliability of electromyography (EMG) activity recorded from the pelvic floor muscles (PFMs) using a new differential suction electrode (DSE) to the reliability of EMG data recorded using other common electrodes, and (ii) motion artifact contamination of EMG activity recorded from the PFMs using the DSE and the Femiscan™ electrode. METHODS: With the DSE and the Femiscan™ in situ, at two separate sessions, each of 20 participants performed three repetitions of a maximum voluntary contraction (MVC) of their PFMs, and 10 repetitions of a maximal effort cough. With Delsys® electrodes located over the biceps brachii, each participant performed three repetitions of a MVC. Between-trial and between-day reliability were assessed using several methods. Motion artifact was assessed by comparing the proportion of contaminated files recorded by each electrode during coughing. RESULT: The DSE was found to have excellent between-trial reliability, as were the Femiscan™ and Delsys® electrodes. Between-day reliability was good for the DSE, but reliability was higher for the Delsys® electrode and the Femiscan™ electrode. The DSE performed better than the Femiscan™ electrode in terms of motion artifact contamination. CONCLUSIONS: The DSE has excellent between-trial reliability and performs better than the Femiscan™ electrode in terms of motion artifact contamination. It does not perform as well as the Femiscan™ electrode in terms of between-day reliability--a result that is not unexpected given the localized region from which the DSE records 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".