Diagnostic criteria for cancer cachexia: reduced food intake and inflammation predict weight loss and survival in an international, multi‐cohort analysis
Bibliographic record
Abstract
BACKGROUND: Cancer-associated weight loss (WL) associates with increased mortality. International consensus suggests that WL is driven by a variable combination of reduced food intake and/or altered metabolism, the latter often represented by the inflammatory biomarker C-reactive protein (CRP). We aggregated data from Canadian and European research studies to evaluate the associations of reduced food intake and CRP with cancer-associated WL (primary endpoint) and overall survival (OS, secondary endpoint). METHODS: The data set included a total of 12,253 patients at risk for cancer-associated WL. Patient-reported WL history (% in 6 months) and food intake (normal, moderately, or severely reduced) were measured in all patients; CRP (mg/L) and OS were measured in N = 4960 and N = 9952 patients, respectively. All measures were from a baseline assessment. Clinical variables potentially associated with WL and overall survival (OS) including age, sex, cancer diagnosis, disease stage, and performance status were evaluated using multinomial logistic regression MLR and Cox proportional hazards models, respectively. RESULTS: Patients had a mean weight change of -7.3% (±7.1), which was categorized as: ±2.4% (stable weight; 30.4%), 2.5-5.9% (19.7%), 6.0-10.0% (23.2%), 11.0-14.9% (12.0%), ≥15.0% (14.6%). Normal food intake, moderately, and severely reduced food intake occurred in 37.9%, 42.8%, and 19.4%, respectively. In MLR, severe WL (≥15%) (vs. stable weight) was more likely (P < 0.0001) if food intake was moderately [OR 6.28, 95% confidence interval (CI 5.28-7.47)] or severely reduced [OR 18.98 (95% CI 15.30-23.56)]. In subset analysis, adjusted for food intake, CRP was independently associated (P < 0.0001) with ≥15% WL [CRP 10-100 mg/L: OR 2.00, (95% CI 1.58-2.53)] and [CRP > 100 mg/L: OR 2.30 (95% CI 1.62-3.26)]. Diagnosis, stage, and performance status, but not age or sex, were significantly associated with WL. Median OS was 9.9 months (95% CI 9.5-10.3), with median follow-up of 39.7 months (95% CI 38.8-40.6). Moderately and severely reduced food intake and CRP independently predicted OS (P < 0.0001). CONCLUSIONS: Modelling WL as the dependent variable is an approach that can help to identify clinical features and biomarkers associated with WL. Here, we identify criterion values for food intake impairment and CRP that may improve the diagnosis and classification of cancer-associated cachexia.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".