Assessing cachexia in obesity: contradiction or perfectly possible?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Existing definitions of clinically important weight loss in patients with cancer do not specifically address weight loss in patients who are obese at presentation. This review explores the clinical impact of weight loss and depletion of the skeletal muscle mass (i.e., criteria defining cancer cachexia), in patients with obesity. RECENT FINDINGS: Overweight and obese BMI values are shown by many recent studies to pose a survival advantage in patients with cancers of advanced stage, when compared with BMI in normal and underweight ranges. The classification of cancer-associated weight loss has evolved, and current grading schemes evaluate the impact of weight across the range of BMI values. Weight loss is associated with mortality in patients with BMI more than 30 kg/m 2 , however this is to a much lesser degree than in patients with lower BMI values. Diagnostic imaging permits the precise assessment of skeletal muscle index (SMI) in patients with cancer, and it has been clearly shown that while usually quite muscular, obese patients can have profound muscle depletion (i.e., sarcopenia), independent of the presence of weight loss. Muscle depletion associates strongly with mortality in obese patients, as well as with complications of cancer surgery and systemic therapy. SUMMARY: It would seem contradictory to diagnose concurrent obesity and cachexia, as these terms represent opposite ends of the weight spectrum. Weight loss can occur in anyone with cancer, however its priority for clinical management may be lesser in obese versus low body weight individuals. Sarcopenic obesity is strongly associated with a poor clinical outcome and deserves further research, diagnosis in clinical practice, and new strategies for mitigation.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".