CACHEXIO: Evaluation of body composition changes and immunotherapy in patients with metastatic cancer.
Bibliographic record
Abstract
209 Background: Given body composition predicts toxicity for patients receiving cytotoxic chemotherapy, we explored changes in body composition and biomarkers as predictors of immune-related adverse events (irAEs) and health care utilization. Methods: We conducted a longitudinal study of patients with metastatic solid tumor receiving immunotherapy (07/2014-10/2017). Eligible patients had a computed tomography (CT) scan prior to first-line immunotherapy with at least two additional CT scans at three, six or nine months after immunotherapy initiation. We analyzed body composition using cross-sectional CT scans at the third lumbar vertebra. We utilized mixed effect linear regression models to assess longitudinal changes in body composition (weight, skeletal muscle, total adipose). We examined associations of baseline body composition and biomarkers (albumin, neutrophil-lymphocyte ratio (NLR)) with the incidence of irAEs and healthcare utilization (hospitalizations/ED visits) using logistic regression. Results: Of 140 patients treated with immunotherapy, 61 met inclusion criteria. The majority (80%) received prior chemotherapy and the most common malignancies included lung (26%), head and neck (20%), and melanoma (20%). We found that one-third (n=19) experienced an irAE and colitis (53%) was the most common irAE. Patients experienced substantial weight loss over time (B= -1.88, p<0.001) with a decrease both in skeletal muscle (B= -3.08, p=0.001) and total adipose tissue (B =-50.44, p<0.001). We found greater skeletal muscle at baseline was associated with lower risk of health care utilization (OR 0.98, 95% CI: 0.965-0.998, p=0.03). We observed no association with biomarkers and/or body composition variables with irAEs or health care utilization. Conclusions: Patients with metastatic cancer receiving immunotherapy lose weight including skeletal muscle and adipose tissue. Aside from higher baseline skeletal muscle predicting less health care utilization, we did not observe any other associations between body composition changes and irAEs or health care utilization.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".