Chemotherapy-Induced Toxicities and Their Associations with Clinical and Non-Clinical Factors among Breast Cancer Patients in Vietnam
Bibliographic record
Abstract
Understanding the burden and factors related to chemotherapy-induced toxicity is important in treatment planning for breast cancer patients. We conducted a prospective study among 396 newly diagnosed and chemotherapy-treated breast cancer patients recruited in two major cancer hospitals in northern Vietnam. Toxicities were captured through medical chart reviews and patient self-reports and graded using NCI CTCAE classification. Associations for sociodemographic and clinical factors with chemotherapy-induced toxicities during first-line chemotherapy were evaluated via multivariable logistic regression. Severe (i.e., grade ≥ 3) hematological (38.6%), and gastrointestinal (12.9%) toxicities were common. A pre-existing nephrological condition was significantly associated with the risk of severe hematological toxicity with adjusted odds ratios (OR) and 95% confidence intervals (CIs) of 2.30 (1.32-4.01). Patients living in rural areas had a lower risk of severe hematological toxicity (OR = 0.48; 95% CI, 0.30-0.77). Patients diagnosed with stage II and stage III-IV had a lower risk of severe gastrointestinal toxicity with ORs and 95% CIs of 0.26 (0.12-0.59) and 0.47 (0.20-1.10), respectively. Triple-negative/basal-like subtype was associated with a higher risk of severe hematological (OR = 3.15; 95% CI, 1.56-6.34) and gastrointestinal toxicities (OR = 3.60; 95% CI, 1.45-8.95) comparing to hormone receptor (HR)-positive HER2-negative subtype. Further research investigating underlying mechanisms would facilitate the development and delivery of personalized treatment and care plans.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".