Body composition metrics as a determinant of trastuzumab deruxtecan related toxicity and response
Bibliographic record
Abstract
Body composition is an important predictor in cancer patients, with skeletal muscle loss and high adiposity associated with poorer prognosis. This study evaluated how body composition affects treatment efficacy in 48 women with metastatic breast cancer receiving trastuzumab deruxtecan. Using computed tomography, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were assessed within 60 days before initiating treatment. High SAT and VAT areas were significantly associated with a higher likelihood of dose reductions (Odds Ratio [OR] = 5.34, p = .032 and OR = 5.52, p = 0.032, respectively). Higher SAT areas correlated with a lower objective response rate (OR = 0.22, p = 0.047). Medium SAT and low/medium VAT densities increased the risk of dose reductions. A body mass index over 25 kg/m 2 was linked to higher dose reductions (OR = 4.97, p = 0.016). These findings emphasize the need for personalized treatment strategies based on body composition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".