Electrical Muscle Stimulation in the Intensive Care Setting
Bibliographic record
Abstract
CONTEXT: The role of electrical muscle stimulation in intensive care has not previously been systematically reviewed. OBJECTIVES: To identify, evaluate, and synthesize the evidence examining the effectiveness and the safety of electrical muscle stimulation in the intensive care, and the optimal intervention variables. DATA SOURCES: A systematic review of articles using eight electronic databases (Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Excerpta Medica Database, Expanded Academic ASAP, MEDLINE, Physiotherapy Evidence Database, PubMed, and Scopus) personal files were searched, and cross-referencing was undertaken. ELIGIBILITY CRITERIA: Quantitative studies published in English, assessing electrical muscle stimulation in intensive care, were included. DATA EXTRACTION AND DATA SYNTHESIS: One reviewer extracted data using a standardized form, which were cross-checked by a second reviewer. Quality appraisal was undertaken by two independent reviewers using the Physiotherapy Evidence Database and Newcastle-Ottawa scales, and the National Health and Medical Research Council Hierarchy of Evidence Scale. Preferred Reporting Items for Systematic Reviews guidelines were followed. RESULTS: Nine studies on six individual patient groups of 136 participants were included. Eight were randomized controlled trials, with four studies reporting on the same cohort of participants. Electrical muscle stimulation appears to preserve muscle mass and strength in long-stay participants and in those with less acuity. No such benefits were observed when commenced prior to 7 days or in patients with high acuity. One adverse event was reported. Optimal training variables and safety of the intervention require further investigation. CONCLUSIONS: Electrical muscle stimulation is a promising intervention; however, there is conflicting evidence for its effectiveness when administered acutely. Outcomes measured are heterogeneous with small sample sizes.
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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 | 0.014 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".