Impact of preoperative sarcopenia on postoperative complications and survival outcomes of patients with esophageal cancer: a meta-analysis of cohort studies
Bibliographic record
Abstract
The effects of preoperative sarcopenia on postoperative complications and survival outcomes of patients undergoing esophageal cancer resection are controversial. From database establishment to 16 May 2021, we systematically searched PubMed, Embase, the Cochrane Library, Web of Science, and Chinese Biomedical Literature Database to collect relevant studies investigating the effects of preoperative sarcopenia on postoperative complications, survival outcomes, and the risk of a poor prognosis of patients undergoing esophagectomy. The Newcastle-Ottawa scale was used to evaluate the quality of the included literature, and RevMan 5.3 software was used for the meta-analysis. A total of 26 studies (3 prospective cohort studies and 23 retrospective cohort studies), involving 4,515 patients, were included. The meta-analysis showed that preoperative sarcopenia significantly increased the risk of overall complications (risk ratio [RR]: 1.15; 95% confidence interval [CI]: 1.08-1.22), pulmonary complications (RR: 1.78; 95% CI: 1.48-2.14), and anastomotic leakage (RR: 1.29; 95% CI: 1.04-1.59) and reduced the overall survival rate (hazard ratio: 1.12; 95% CI: 1.04-1.20) following esophageal cancer resection. Preoperative sarcopenia increased the risks of overall postoperative and pulmonary complications in patients undergoing esophageal cancer resection. For patients with esophageal cancer, assessing the preoperative risk of preoperative sarcopenia is necessary.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.051 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".