Low Muscle Mass and Mortality in Patients with Sars-Cov-2: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Aim Low muscle mass assessed by computed tomography (CT) may be associated with mortality or admission to the Intensive Care Unit (ICU) of patients with COVID-19. Materials and Methods: Data were collected through searches in PubMed/MEDLINE and EMBASE using the Rayyan tool to screen identified studies, and the review followed the PRISMA model. Data extraction was performed by two authors independently, and the risk of bias was assessed using the Newcastle-Ottawa quality tool. Statistical analyses were performed using R version 3.5.2 (The R Foundation for Statistical Computing) and Review Manager (RevMan 5.3. Copenhagen: The Nordic Cochrane Center) software. Results: Eighteen observational studies met the inclusion criteria for qualitative analysis, one of which was excluded due to a high risk of bias. Fifteen studies were included in the meta-analysis, totaling 3,920 patients and 640 deaths, which demonstrated that individuals with low muscle mass are 2.40 times more likely to die. When admission to the Intensive Care Unit (ICU) was considered an outcome, eight studies were included, totaling 2,993 patients, of which 770 required intensive care support, with low muscle mass increasing the chances of admission by 1.99 times in the ICU. Conclusion: Based on the results shown in the present study, low muscle mass assessed by CT suggests an association with higher mortality and ICU admission in patients with COVID-19.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".