Baseline characteristics as predictors of adjuvant chemotherapy (AC) toxicities in stage III colorectal cancer (CRC) patients.
Bibliographic record
Abstract
e18237 Background: Toxicities acquired from AC can impact quality of life. Baseline patient characteristics such as age, sex, ECOG, tumor location, TNM staging, and comorbidities are frequently considered in AC treatment decision-making, but their associations with common toxicity outcomes remain unclear. Methods: We used a population-based cohort of CRC patients to test our hypothesis. We reviewed individuals treated with adjuvant monotherapy (Capecitabine) or combination therapy (FOLFOX or CAPOX) within 12 weeks of curative resection at any 1 of 6 cancer centers in British Columbia, and determined the associations between baseline characteristics and toxicity outcomes. Results: Among 371 patients, median age was 65 (range 26-86) years, 51.5% were men, and 14% were ECOG ≥2. In this cohort, 41% received monotherapy and 59% received combination therapy. For monotherapy, univariate analyses found that age, sex, ECOG, and pre-treatment anemia were associated with hematological toxicities ( P < 0.05). Likewise, tumor location and TNM staging were associated with gastrointestinal (GI) toxicities ( P < 0.05). On multivariate analyses, hematological toxicities were more likely to develop with old age (≥70) (OR 3.30, 95% CI 1.17-9.37, P= 0.025) and pre-treatment anemia (OR 23.18, 95% CI 6.36-84.48, P= 0.001), while GI toxicities were less likely to occur with a left-sided tumor (OR 0.38, 95% CI 0.15-0.99, P= 0.047). In univariate analyses of combination therapy, sex and pre-treatment anemia were also associated with hematological toxicities, while cardiac and/or respiratory comorbidities were associated with neuropathy ( P < 0.05). In multivariate analyses, however, only female sex was predictive of hematological toxicities (OR 5.13, 95% CI 2.08-12.68, P= 0.001) and neuropathy was less likely to develop with cardiac and/or respiratory comorbidities (OR 0.23, 95% CI 0.07-0.81, P= 0.023). Further analyses did not show any consistent correlations between other baseline characteristics and toxicity outcomes. Conclusions: Readily available baseline patients characteristics are associated with the development of specific side effects, which can be used to better inform AC discussions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".