Muscle and Adipose Wasting despite Disease Control: Unaddressed Side Effects of Palliative Chemotherapy for Pancreatic Cancer
Bibliographic record
Abstract
Muscle and adipose wasting during chemotherapy for advanced pancreatic cancer (aPC) are associated with poor outcomes. We aimed to quantify the contributions of chemotherapy regimen and tumour progression to muscle and adipose wasting and evaluate the prognostic value of each tissue loss. Of all patients treated for aPC from 2013–2019 in Alberta, Canada (n = 504), computed-tomography (CT)-defined muscle and adipose tissue index changes (∆SMI, ∆ATI, cm2/m2) were measured for patients with CT images available both prior to and 12 ± 4 weeks after chemotherapy initiation (n = 210). Contributions of regimen and tumour response to tissue change were assessed with multivariable linear regression. Survival impacts were assessed with multivariable Cox’s proportional hazards models. Tissue changes varied widely (∆SMI: −17.8 to +7.3 cm2/m2, ∆ATI: −106.1 to +37.7 cm2/m2) over 116 (27) days. Tumour progression contributed to both muscle and adipose loss (−3.2 cm2/m2, p < 0.001; −12.4 cm2/m2, p = 0.001). FOLFIRINOX was associated with greater muscle loss (−1.6 cm2/m2, p = 0.013) and GEM/NAB with greater adipose loss (−11.2 cm2/m2, p = 0.002). The greatest muscle and adipose losses were independently associated with reduced survival (muscle: HR 1.72, p = 0.007; adipose: HR 1.73, p = 0.012; tertile 1 versus tertile 3). Muscle and adipose losses are adverse effects of chemotherapy and may require regimen-specific management strategies.
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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 | 0.001 |
| 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) | 0.000 | 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".