PS-C25-7: PROGNOSTIC VALUE OF COMPUTED TOMOGRAPHY PSOAS MUSCLE SIZE IN PREDICTING MAJOR ADVERSE CARDIOVASCULAR EVENTS IN THE ELDERLY: A META-ANALYSIS.
Bibliographic record
Abstract
Background: Cardiovascular diseases (CVD) are the leading cause of death globally (WHO). It accounts for > 17 million deaths each year (30% of all deaths) (WONG, 2014). Aging is associated with body composition changes such as a reduction in the muscle mass and an increase in visceral fat that shift the metabolism to an unfavorable state which increases the risk of CVD (EVANS, 2021). Psoas muscle mass index (PMI) has been growing recognition as an objective and quantitative marker to assess muscle wasting and has been suggested as a useful predictive marker for long-term outcome after cardiovascular surgery (MATSUMOTO, 2020). The aim of this study is to determine the prognostic value of psoas muscle size for development of major adverse cardiovascular events. Methodology: We searched PubMed and Google scholar. The Newcastle-Ottawa Scale assessment was used for quality assessment. Two investigators independently extracted patient baseline characteristics, CT psoas muscle size, and research endpoint such as all-cause mortality, myocardial infarction, and stroke. The hazard ratio (95% CI) and relative risk (95% CI) was calculated using a generic inverse variance approach. The overall effects was determined by Z-test and P-value < 0.05 were considered as statistically significant. Results: Out of 37 articles, 10 citations fulfilled the inclusion criteria. Sarcopenia was not associated with MACE (HR 14.6, 95% CI:5.69 - 37.48, p value < 0.00001, I2 0%) and all cause mortality (RR 2.04, 95% CI:1.55 - 2.69, p value of < 0.00001, I2 0%). The sub-group analysis shows no association between sarcopenia and MACE among patients > 60 years (HR 15.36, 95% CI: 5.54 - 42.53, p value < 0.0001, I2 0%), myocardial infarction (RR 1.96, 95% CI: 0.63 - 6.10, p value of < 0.24, I2 0%), and stroke (RR 1.62, 95% CI: 0.84 - 3.15, p value of < 0.15, I2 0%). Conclusion: Sarcopenia is not associated with MACE and all cause mortality. Among patients > 60 years, sarcopenia shows no association between myocardial infarction and stroke. Further studies should be done to determine the cut-off value for psoas muscle size for sarcopenia and sarcopenic obesity among elderly patients.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.039 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".