Inclusion criteria for cancer cachexia clinical trials: CT-defined skeletal muscle loss versus body weight loss.
Bibliographic record
Abstract
67 Background: Cancer cachexia is defined by skeletal muscle loss, with or without fat loss (Fearon et al 2011); however, inclusion criteria for cachexia clinical trials requires a defined weight loss over time rather than muscle loss. We hypothesized that cross sectional imaging may reveal the presence of cachexia otherwise obscured by fat mass changes. Methods: A retrospective analysis of longitudinal CT scans was performed in metastatic colorectal cancer (mCRC) patients screened for a cancer cachexia trial, which required ≥5% weight loss in the prior 6 mos. De-identified CT images were analyzed for total muscle, subcutaneous, and visceral fat cross-sectional areas (cm2) at the 3rd lumbar vertebra at baseline and up to 12 mos prior (Lieffers et al 2009). Logistic regression was used to test differences between patients with <5% vs ≥5% weight loss. Random intercept regression was used to evaluate significant trends in CT measures over time. Results: 42 mCRC patients were screened and 3(7%) enrolled. Patients were excluded for comorbidity/contraindication 14 (33%), excessive [>20%] weight loss 4 (9.5%), and insufficient [<5%] weight loss 19 (45%). For the <5% weight loss subset, there was a mean of 6.7 CT scans (SD=2.67) and of 9% (SD=5.4, min=0%, 25th percentile=4.9%) mean max muscle loss. Notably this group was simultaneously losing muscle (p=0.002) and gaining visceral adipose (p=0.007). For the ≥5% weight loss subset, there was a mean of 7.5 CT scans (SD=4.5) and 20% (SD=10.0, min=5.2%, 25th percentile =10.6) mean max muscle loss. Greater max muscle loss increased the odds of being in the ≥5% weight loss subset (OR=1.19, 95% CI: 1.06,1.33). This group also had a significant decrease in visceral adipose over time (p<0.001). Redefined inclusion criteria of ≥5% muscle loss would have included 14 of the 19 patients excluded because of <5% weight loss. Conclusions: Defining cancer cachexia as weight loss over time may be limited as it does not capture body composition changes and hinders trial accrual. Cross-sectional CT body composition analysis may improve early detection of muscle loss and improve trial accrual.
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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 | 0.042 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".