Bibliographic record
Abstract
<div><p>Background</p><p>Surface Electromyography (sEMG) has been used to monitor respiratory muscle function and contractility in several clinical situations, however there is the lack of standardization for the analysis and processing of the signals.</p><p>Objective</p><p>To summarize the respiratory muscles most assessed by sEMG in the critical care setting and the assessment procedure details employed on those muscles regarding electrode placement, signal acquisition, and data analysis.</p><p>Methods</p><p>A systematic review of observational studies was registered on PROSPERO (number CRD42022354469). The databases included PubMed; SCOPUS; CINAHL, Web of Science and ScienceDirect. Two independent reviewers ran the quality assessment of the studies using the Newcastle-Ottawa Scale and Downs & Black checklists.</p><p>Results</p><p>A total of 311 participants were involved across the 16 studies, from which 62.5% (10) assessed the diaphragm muscle and 50% (8) assessed the parasternal muscle with similar electrode placement in both of them. We did not identify common patterns for the location of the electrodes in the sternocleidomastoid and anterior scalene muscles. 12/16 reported sample rate, 10/16 reported band-pass and 9/16 reported one method of cardiac-interference filtering technique. 15/16 reported Root Mean Square (RMS) or derivatives as sEMG-obtained variables. The main applicabilities were the description of muscle activation in different settings (6/16), testing of reliability and correlation to other respiratory muscles assessment techniques (7/16), and assessment of therapy response (3/16). They found sEMG feasible and useful for prognosis purposes (2/16), treatment guidance (6/16), reliable monitoring under stable conditions (3/16), and as a surrogate measure (5/16) in mechanically ventilated patients in elective or emergency invasive procedures (5/16) or in acute health conditions (11/16).</p><p>Conclusions</p><p>The diaphragm and parasternal muscles were the main muscles studied in the critical care setting, and with similar electrodes placement. However, several different methods were observed for other muscles electrodes placement, sEMG signals acquisition and data analysis.</p></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.289 | 0.023 |
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; both teacher heads agree on what is shown here.
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".