Clinical significance of sarcopenia in elderly patients undergoing endoscopic submucosal dissection - A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background and Aims: As global life expectancy rises and gastrointestinal tumor incidence increases, more elderly patients are undergoing endoscopic submucosal dissection (ESD) for tumor treatment. This highlights the importance of sarcopenia assessment before ESD. This systematic review and meta-analysis aims to assess sarcopenia's role in predicting post-ESD adverse outcomes in the elderly. Methods: We conducted a systematic review and meta-analysis to investigate the impact of sarcopenia on the prognosis of elderly patients undergoing ESD treatment. A comprehensive search was conducted across three databases (PubMed, Embase, Web of Science). Using NEWCASTLE - OTTAWA ASSESSMENT SCALE for risk of bias assessment. The data were synthesized using Review Manager 5.3. Results: A total of 9 reports were identified, analyzing 7 indicators, with a combined sample size of 6044. Through a series of analyses, we have derived several highly credible research findings: the overall OR and 95% CI for gastric and colorectal post-ESD perforation between sarcopenia and nonsarcopenia groups were 1.34 [0.92, 1.97], for CTCAE grade > 2 were 2.65 [1.45, 4.82], for gastric post-ESD pneumonia were 1.90 [1.24, 2.90], and for gastric post-ESD mortality within 5 years were 2.96 [1.33, 6.58]. Conclusions: Sarcopenia is a risk factor for increased incidence of complications (CTCAE > 2) after undergoing gastric and colorectal ESD, increased pneumonia rates and higher mortality rates within five years following gastric ESD treatment in elderly patients. However, sarcopenia does not lead to an increased perforation rate in elderly patients undergoing gastric and colorectal ESD treatments.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.030 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".