Quality of reporting masticatory muscle electromyography in 2004: a systematic review
Bibliographic record
Abstract
This study evaluated the quality of reporting electromyography in studies evaluating the masticatory muscles published during 2004. Several electronic databases were searched. Abstracts and later articles were selected by a consensus from the five reviewers. An adaptation of the methodological checklist published by the International Society of Electrophysiology and Kinesiology (ISEK) was used. The following information regarding electrodes was reported on the 35 finally selected articles: location (94.3%), interelectrode distance (48.6%), and material (42.9%); detection and amplification: amplification type (54.3%), gain (25.7%), low high pass filters (60%) and cut-off frequencies (60%); electromyography (EMG) processing: sampling rate (74.2%), rectification (46.6%), root-mean square (RMS) (39.2%); number of bits and model of A/D card (17.1%); and normalization procedure (40%). Reasons for the poor reporting are discussed. Because of the general poor quality of reporting of the analysed studies, findings of studies using surface electromyography of masticatory muscles should be interpreted with caution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.101 | 0.376 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".