Functional assessment of the pelvic floor muscles by electromyography: is there a normalization in data analysis? A systematic review
Bibliographic record
Abstract
ABSTRACT This study aims to evaluate the method of analysis of electromyographic data considering the functional assessment of pelvic floor muscles (PFM). We have included in our search strategy the following databases: Medline, PubMed, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews, PEDro, and IBECS, considering articles published in the last ten years (2004-2014). The identified articles were independently examined by two evaluators, according to these inclusion criteria: (1) population: female adults; (2) PFM assessment by electromyography (EMG) with vaginal/anal probe; and (3) description of how electromyographic data analysis is performed. The Newcastle-Ottawa Scale (NOS) was used to assess the risk of bias. We identified 508 articles, of which 23 were included in the review. The data showed differences between the collection protocols, and a significant number of studies did not normalize the electromyographic data. Physiotherapists are among the clinicians who most frequently use EMG to evaluate the function and dysfunction of the neuromuscular system. Although some previous studies have provided an overview to guide the evaluator in the assessment, few succeeding studies followed their recommendations.
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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 | 0.115 | 0.355 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.012 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".