Impact of body measurements (BM) on overall survival (OS) and quality of life (QoL) in real-world patients (pts) with metastatic esophageal cancer.
Bibliographic record
Abstract
4544 Background: Body fat and muscle influence prognosis in many cancer types. However, this association is unclear for real-world MEC pts, who are often in worse performance status than their previous baseline. In addition, the relationship of BM at presentation and QoL is unknown. We used real-world MEC pts to assess the importance of BM in OS and QoL. Methods: BM were done at baseline computed tomography in MEC pts, treated from 2006-2014 at the Princess Margaret Cancer Centre. Two radiologists (correlation 0.9-1.0) assessed L3 level using SliceOMatic to determine Skeletal Muscle Index (SMI - muscle area at L3 normalized by height), Visceral Adiposity Tissue (VAT), and Subcutaneous Adiposity Tissue (SAT). We used previously published cut-offs for sarcopenia based on sex and BMI, and the highest tertile as the cut-off for adiposity. We used prospectively collected QoL surveys including EuroQol 5D-5L (EQ5D) and the Functional Assessment of Cancer Therapy – Esophageal (FACT-E). Results: Of 200 pts, 164 (82%) were male, 180 (92%) were non-Asian; mean age was 62 y; ECOG: 0-1 = 142 (71%), 2 = 58 (29%); 69% had adenocarcinoma; 5% were underweight, 44% normal weight, 30% overweight, and 21% obese. 40 (20%) pts completed QoL measures. We found that 104 (52%) were sarcopenic at baseline, 66 (33%) had high VAT, and 67 (34%) had high SAT. A multivariable Cox model showed that sarcopenia and VAT were independent prognostic variables for three-year OS: sarcopenia increased the risk of death by 50% (adjusted hazard ratio, aHR 1.50, p 0.02), whereas every 100-cm2 increase in VAT improved OS by 24% (aHR 0.76, p 0.03). Finally, sarcopenic pts had significantly worse physical well-being (p 0.01) on FACT-E after adjusting for sex and age. Numerically, the EQ5D also showed lower scores in sarcopenic pts but this was not statistically significant (p 0.18). Conclusions: In MEC pts, sarcopenia and low visceral adiposity result in worse OS; sarcopenia is also significantly associated with poor QoL. Future work will need to focus on potential rehabilitation strategies such as nutritional support and exercise training to offset the poor prognosis associated with sarcopenia and reduced adiposity. [Table: see text]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".