The Cachexia Clinic: From Staging to Managing Nutritional and Functional Problems in Advanced Cancer Patients
Bibliographic record
Abstract
Panels of experts have agreed on the definition of cancer cachexia stages (CCS). We evaluated the clinical relevance of these stages and proposed ways to apply the CCSs to the clinical practice via standardized methods. The CCS were applied to 207 patients with advanced non-small-cell lung or gastrointestinal cancers from the Human Cancer Cachexia Database via consensus among the coauthors. Patients were categorized as noncachectic, precachectic, cachectic, and in refractory cachexia. Through analysis of variance, χ2, and Kaplan-Meier analyses, we tested the relationships between CCSs and selected outcomes. The CCS were significantly correlated (P < 0.05) with patient-centered indicators, including overall symptom burden, quality of life, tolerability to chemotherapy, body composition, hospital stay, and survival. However, precachectic and cachectic patients behaved similarly in all these outcomes but were significantly different from noncachectic and refractory cachectic patients. The average scores for the abridged Patient-Generated Subjective Global Assessment (aPG-SGA) corresponded well with the CCSs and reflected nicely the cut-off scores recommended for the nutritional triage of cancer patients. In addition to the aPG-SGA, we proposed the Edmonton Symptom Assessment System and routine laboratory tests for the triage of patients with refractory cachexia, for whom comfort care is the primary goal. The cachexia clinic hosted by the McGill Nutrition and Performance Laboratory offers a model for diagnosing more precisely the pathophysiology and severity of precachectic and cachectic conditions. The cachexia clinic, by working closely with palliative care programs, may offer the best environment for a comprehensive and personalized approach to the nutritional and functional problems in advanced cancer 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".