Sarcopenia as a predictor of mortality in advanced pancreatic cancer.
Bibliographic record
Abstract
268 Background: Pancreatic cancer (PC) patients have multiple risk factors for malnutrition including digestive enzyme deficiency, gastric outlet obstruction and post prandial abdominal pain. In order to assess prognosis and triage patients at risk for malnutrition, accurate nutrition assessment is crucial. Overweight (Ow) or obese (Ob) patients pose a particular challenge and weight status should not be the only determining nutritional assessment tool. Sarcopenia (Sp) is defined as muscle mass (MM) two standard deviations below the healthy adult mean. It has been demonstrated in multiple studies to be a more accurate indicator of nutritional status. Sp is associated with poor clinical outcomes and lower survival in other conditions such as cirrhosis. Radiologic assessment of MM can be obtained reliably from computed tomography (CT) scans. Methods: All patients with advanced PC who had baseline CT scans were included in this study. MM assessment was performed using an automated software (SliceOmatic, Tomovision, Montreal) and the skeletal muscle index(SMI) was calculated at level of the 3rd lumbar (L3) vertebrae. Multiple patient characteristics were assessed including demographics, weight, body mass index (BMI), cancer stage, treatment received and survival. Sp was defined as an L3 SMI of less than 38.5 in women and 52.4 in men. Survival analysis was performed using SAS software and Log-rank and Wilcoxon statistics. Results: 167 patients, 49% female, 62% with sarcopenia (S+), 44% Ow/Ob (O+) and 14% sarcopenic and Ow/Ob (S+O+). Patients received a variety of treatments including surgery, chemotherapy and radiation. There was a trend towards lower survival in S+O+ patients while patients who were S-O+ survived the longest. Median survival in months was 6.2 for S+O+, 7.1 for S+O-, 7.8 for S-O- and 10 for S-O+. Conclusions: Patients with sarcopenia appear to have decreased survival especially if overweight/obese. This suggests that overweight/obese pancreatic cancer patients with sarcopenia may derive the most benefit from aggressive nutritional interventions. Well powered prospective studies are needed to confirm this observation and study the effects of aggressive nutritional interventions in this subgroup of patients.
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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 | 0.001 |
| 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) | 0.002 | 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".