Two faces of drug therapy in cancer: drug-related lean tissue loss and its adverse consequences to survival and toxicity
Bibliographic record
Abstract
PURPOSE OF REVIEW: A common feature of cancer patients is loss of lean tissue, specifically skeletal muscle, which may be the result of the tumor or a side-effect of chemotherapy or other drugs. Lean tissue loss in turn has important adverse implications for toxicity of antineoplastic therapy and, hence, cancer prognosis. RECENT FINDINGS: Contemporary cancer populations have heterogeneous proportions of lean tissue, regardless of body weight. Wasting of lean tissue during the cancer trajectory has been associated with tumor progression. Lean tissue depletion is an independent predictor of severe toxicity in patients treated with chemotherapeutic agents of diverse classes. Patients with lean tissue depletion behave as if overdosed and have toxicity of sufficient magnitude to require dose reductions, treatment delays or definitive termination of treatment. Muscle loss may occur due to a specific effect of a chemotherapy agent (i.e. sorafenib), androgen suppression therapy or other drugs (i.e. statins such as atorvastatin). SUMMARY: Lean tissue wasting occurs due to cancer progression and may be exacerbated by several drug classes. This loss of lean tissue is not proportional to changes in body weight and is prognostic of enhanced treatment toxicity and reduced survival.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".