Association of body composition, tumor-specific assessment, and patient demographics at diagnosis with 90-day and overall survival in esophageal cancer patients in a global population
Bibliographic record
Abstract
This study seeks to define baseline variation and clinical correlates of body composition in a large international cohort of patients undergoing esophagectomy for cancer. Patients who underwent esophagectomy in 14 high-volume centers between 2007 to 2019 were eligible for inclusion. Skeletal muscle, visceral and subcutaneous adipose tissues within computer tomography images (L3 axial image), acquired routinely at diagnosis, were analyzed in accordance with a standardized protocol. In total, 1716 patients were recruited from three global regions: North America (22%), Europe (55%), and Asia (23%). Patients were predominantly male (79.5%) and adenocarcinoma was the most common histological subtype (66.6%). Characteristics significantly associated with levels of muscle and adiposity were global region, sex, age, and histological subtype (P < 0.001). Compared to adenocarcinoma, squamous cell carcinoma was associated with significantly lower levels of muscle and adiposity, a finding that was independent of global region, sex, and age using a multivariable linear regression model (P < 0.001). Reduced skeletal muscle and an excess of total adiposity at diagnosis was associated with increased 90-day mortality and reduced long-term survival. A prediction model including skeletal muscle, total adiposity at diagnosis and other tumor and patient specific variables was constructed to allow convenient survival prediction. This study adopts a standardized method to define international variation in parameters of body composition in esophageal cancer patients. Findings provide clinically relevant information regarding operative mortality and overall survival and can inform future guidelines for the use of body composition assessment in routine clinical practice.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".