Sarcopenia in an Overweight or Obese Patient Is an Adverse Prognostic Factor in Pancreatic Cancer
Bibliographic record
Abstract
PURPOSE: The average weight-losing pancreatic cancer patient undergoing palliative therapy is frequently overweight rather than underweight, and this can confound conventional measures used for risk stratification. The aim of this study was to evaluate if weight and body composition, specifically sarcopenia, assessed from diagnostic computed tomography (CT) scans, is of prognostic value in patients with pancreatic cancer. The nature and extent of tissue loss over subsequent months was also evaluated. EXPERIMENTAL DESIGN: A total of 111 patients entering a palliative therapy program, who had CT images and had undergone nutritional screening, were studied. In patients for whom follow-up scans were available (n = 44), longitudinal changes in body composition were studied at a mean of 230 +/- 62 and 95 +/- 60 days prior to demise. RESULTS: Sixty-two patients (55.9%) were sarcopenic, 44 (39.6%) were overweight/obese, and 18 (16.2%) were both. Age > or =59 years (hazard ratio, 1.71; 95% confidence interval, 1.10-2.66; P = 0.018), and overweight/obese sarcopenia (hazard ratio, 2.07; 95% confidence interval, 1.23-3.50; P = 0.006) were identified as independent predictors of survival on multivariate analysis. Longitudinal analysis revealed that total fat-free mass index decreased from 15.5 +/- 2.5 kg/m(2) to 14.5 +/- 2.0 kg/m2 (P = 0.002), and total fat mass index decreased from 7.5 +/- 2.0kg/m2 to 6.0 +/- 1.5kg/m2 (P < 0.0001) over 135 days. CONCLUSIONS: Sarcopenia in overweight/obese patients with advanced pancreatic cancer is an occult condition but can be identified using CT scans. This condition is an independent adverse prognostic indicator that should be considered for stratification of patients' entering clinical trials, systemic therapy, or support care programs.
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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 | 0.000 | 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".