Markers of sarcopenia increase 30-day mortality following emergency laparotomy: A systematic review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Decreased skeletal muscle mass and quality are one of the several markers used for sarcopenia diagnosis and are generally associated with increased rates of post-operative infections, poorer recovery and increased mortality. The aim of this review was to evaluate methods applied to detect markers of sarcopenia and the associated outcomes for patients undergoing emergency laparotomy. METHODS: This review was conducted with reference to Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. MEDLINE, Embase and Google Scholar databases were searched. Studies detecting patients with sarcopenia or skeletal muscle decline markers and the associated outcomes after emergency laparotomy surgery were considered. The Newcastle-Ottawa Scale was used to evaluate publication quality. RESULTS: = 967. The age range was 36-95 years. There were 1107 females (53%) and 973 males (47%) across all 7 studies. All studies measured psoas muscle mass and three studies assessed psoas muscle quality using computerized tomography (CT) imaging. No study assessed muscle strength or function, while five studies showed an association between low muscle mass and increased mortality rates after emergency laparotomy. Among the three studies, which assessed muscle quality, two of three studies showed poorer 30-day survival rates. CONCLUSIONS: The existing literature is limited, however it indicates that low psoas muscle mass and quality markers are associated with increased 30-day mortality rates after emergency laparotomy. Therefore, muscle markers can be used as a new feasible tool to identify most at risk patients requiring further interventions.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".