A validation study of the cancer cachexia stages
Bibliographic record
Abstract
Introduction: Cachexia is a highly prevalent syndrome in cancer and other chronic diseases.The taxonomy of cancer cachexia (CC) is complex because of the heterogeneous pathophysiological and clinical features.Lately, a consensual definition and a classification system comprising four CC stages have been proposed but not yet validated.The aim of our study was to classify advanced cancer patients in the CC stages to determine the association between these stages and clinical, nutritional and functional outcomes.Methods: Starting from the four-stage classification system proposed for CC [non-cachexia (NC), pre-cachexia (PC), cachexia (C) and refractory cachexia (RC)], we identified five classification criteria available in clinical routine practice [biochemistry (elevated C-reactive protein or leukocytes, or hypoalbuminemia, or anemia), food intake (normal/decreased), moderate (≤ 5%) or significant weight loss (> 5%/past six months) and reduced performance status], to allocate patients in the CC stages.Thereafter, we determined if clinical, nutritional and functional characteristics varied significantly across patients re-grouped in the different CC stages.Results: Our sample consisted of 297 advanced cancer patients, of whom 69% had metastatic disease, mainly from primary gastrointestinal and lung tumours.These patients were classified into C (36%), followed by 21% for PC and RC and 15% for NC.Significant differences were observed among the CC stages for most of the outcomes (symptoms, body composition, handgrip strength, emergency room visits and length of hospital stays) according to the severity of CC.Survival analysis showed differences among all stages except between PC and C. Discussion: The proposed set of five criteria enabled us to classify patients into distinct CC stages associated with relevant outcomes.However, the lack of difference between PC and C suggests that PC is a more heterogeneous group including patients at high risk of cachexia as well Matthias Figueira, mi Querido, for his tremendous, unwavering and unflagging support, his patience and understanding and for believing in me, I love you.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".