Prognostic significance of nutritional markers in metastatic gastric and esophageal adenocarcinoma
Bibliographic record
Abstract
Abstract Background Malnutrition and sarcopenia are poor prognostic factors in many cancers. Studies in gastric and esophageal (GE) cancer have focused on curative intent patients. This study aims to evaluate the prognostic utility of malnutrition and sarcopenia in de novo metastatic GE adenocarcinoma. Methods Patients with de novo metastatic GE adenocarcinoma seen at the Princess Margaret Cancer Centre from 2010 to 2016 with an available pre‐treatment abdominal computed tomography (CT) were included. Malnutrition was defined as nutritional risk index (NRI) <97.5. Skeletal muscle index (SMI) was measured at the L3 level (sarcopenia defined as SMI <34.4 cm 2 /m 2 in women and <45.4 cm 2 /m 2 in men). Patients receiving chemotherapy had NRI and SMI recalculated at the time of first restaging CT. Results Of 175 consecutive patients, 33% were malnourished and 39% were sarcopenic at baseline. Patients with pretreatment malnourishment had significantly shorter overall survival (OS; 5.8 vs. 10.9 months, p = 0.000475). Patients who became malnourished during chemotherapy had worse OS compared to those who maintained their nutrition (12.2 vs. 17.5 months p = 0.0484). On univariable analysis, ECOG ( p < 0.001), number of metastatic sites ( p = 0.029) and NRI ( p < 0.001) were significant prognostic factors while BMI ( p = 0.57) and sarcopenia ( p = 0.19) were not. On multivariable analysis, ECOG ( p < 0.001), baseline NRI ( p = 0.025), and change in NRI during treatment ( p < 0.001) were significant poor prognostic factors for OS. Conclusions In de novo metastatic GE adenocarcinoma patients, ECOG, pretreatment NRI and change in NRI were significant prognostic factors for OS while sarcopenia was not. Use of NRI at baseline and during treatment can provide useful prognostic information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".