The rate and assessment of muscle wasting during critical illness: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patients with critical illness can lose more than 15% of muscle mass in one week, and this can have long-term detrimental effects. However, there is currently no synthesis of the data of intensive care unit (ICU) muscle wasting studies, so the true mean rate of muscle loss across all studies is unknown. The aim of this project was therefore to systematically synthetise data on the rate of muscle loss and to identify the methods used to measure muscle size and to synthetise data on the prevalence of ICU-acquired weakness in critically ill patients. METHODS: We conducted a systematic literature search of MEDLINE, PubMed, AMED, BNI, CINAHL, and EMCARE until January 2022 (International Prospective Register of Systematic Reviews [PROSPERO] registration: CRD420222989540. We included studies with at least 20 adult critically ill patients where the investigators measured a muscle mass-related variable at two time points during the ICU stay. We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and assessed the study quality using the Newcastle-Ottawa Scale. RESULTS: Fifty-two studies that included 3251 patients fulfilled the selection criteria. These studies investigated the rate of muscle wasting in 1773 (55%) patients and assessed ICU-acquired muscle weakness in 1478 (45%) patients. The methods used to assess muscle mass were ultrasound in 85% (n = 28/33) of the studies and computed tomography in the rest 15% (n = 5/33). During the first week of critical illness, patients lost every day -1.75% (95% CI -2.05, -1.45) of their rectus femoris thickness or -2.10% (95% CI -3.17, -1.02) of rectus femoris cross-sectional area. The overall prevalence of ICU-acquired weakness was 48% (95% CI 39%, 56%). CONCLUSION: On average, critically ill patients lose nearly 2% of skeletal muscle per day during the first week of ICU admission.
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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 | 0.037 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".