PS02.248: ASSESSMENT OF BODY COMPOSITION AND SARCOPENIA IN PATIENTS WITH ESOPHAGEAL CANCER: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background There has recently been increased interest in the assessment of body composition in patients with esophageal cancer for the purpose of nutritional evaluation and prognostication. This systematic review and meta-analysis intends to summarize and critically evaluate the current literature concerning the assessment of body composition in patients with esophageal cancer and to assess its potential implication upon early and late outcomes. Methods A systematic literature search (up to August-2017) was conducted for studies describing the assessment of body composition in patients with esophageal and gastroesophageal junctional cancer. Meta-analysis of postoperative outcomes including long-term survival was performed using random effects models. Results C Twenty-nine studies reported the assessment of body composition in 3193 patients. Methods used to assess body composition in patients with esophageal cancer included: computerized tomography (n = 18 studies); bioelectrical impedance analysis (n = 10), and; dual-energy x-ray absorptiometry (n = 1). Significant variability was observed in regard to study design and the criteria used to define individual parameters of body composition. Sarcopenic patients had a higher incidence of postoperative pulmonary complications (7 studies, OR 2.03, 95%-CI 1.32 to 3.11, P = 0.001) after esophagectomy. Meta-analysis of six studies presenting long-term outcomes after esophagectomy identified significantly worse survival in patients who were sarcopenic (HR 1.70, 95%-CI 1.33 to 2.17, P < 0.0001; Figure 1). Conclusion The assessment of body composition has the potential to become a clinically useful tool that could support decision-making in patients with esophageal cancer. Current evidence is however weakened by inconsistencies in methods of assessing and reporting body composition in this patient group. Disclosure All authors have declared no conflicts of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".