Bibliographic record
Abstract
PURPOSE OF REVIEW: Cachexia limits cancer therapy, quality of life, and survival of patients with cancer. Challenges identifying and diagnosing cachexia are due to disparities in diagnostic criteria. A framework for classification of cancer cachexia was recently defined by international consensus. This review describes recent efforts to use this framework to develop definitive diagnostic criteria for cancer cachexia. RECENT FINDINGS: The principle proposed in the cancer cachexia framework for development of diagnostic criteria is that 'definitive cutoffs for variables could be determined from large contemporary datasets by determining the values that relate optimally to meaningful patient-centered outcomes.' Clearly defined statistical methods to examine distributions of diagnostic criteria in relation to an outcome are used to achieve this task. As a first step, a dataset of more than 11 000 cancer patients from Europe and Canada was compiled, and used to develop and validate a new grading system for cancer-associated weight loss, based on a risk stratification with survival as the outcome. The next refinements for diagnostic criteria will be enabled by the emergence of rich datasets including key variables further specifying the nature of cachexia such as skeletal muscle depletion, reduced food intake, and inflammation. SUMMARY: Development of diagnostic criteria for cancer cachexia is based on a solid conceptual foundation and is moving toward defining the type of assessments, optimal values, and combinations of criteria that best define cachexia. Large contemporary datasets representing different cancer populations, candidate cachexia diagnostic criteria, and clinical outcomes will further ensure developmental and validation efforts.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".