Racial differences in acute toxicities of FEC 100 chemotherapy in patients with breast cancer
Bibliographic record
Abstract
e11515 Background: Racial disparities in breast cancer outcomes are attributed to differences in baseline tumor characteristics, stage, and socioeconomic factors. However, little is known about racial differences in treatment-related toxicities. We hypothesized that racial and ethnic differences result in differential tolerance to chemotherapy and possibly compromise to the dose intensity of adjuvant/neoadjuvant chemotherapy. Methods: Data was collected from 4 international collaborating centers (University of Miami, JBCRG (Japan Breast Cancer Research Group), University of Hong Kong, and Tom Baker Cancer Center) at which patients of different ethnic background have been treated for non metastatic breast cancer with same adjuvant or neoadjuvant chemotherapy of FEC 100: fluorouracil 500 mg/m2, epirubicin 100 mg/m2, and cyclophosphamide 100 mg/m2). Racial/ethnic differences in toxicities were assessed by first episode of grade 2 or higher toxicity. Analysis of data was performed at the University of Miami. Results: Treatment-related toxicities are compared according to four race/ethnicity groups (120 Caucasian from USA and Canada (C), 16 African American (AA) from USA and Canada, 141 Japanese Asian (JA), and 23 Asian from Hong Kong (HKA)) (Table). JA and HKA had a significant higher rate of grade 3 or higher toxicity compared with C or AA women; 65%, 61%, 28%, and 31% respectively. However, there were no significant differences in chemotherapy dose intensity or density across the 4 race/ethnicity groups. Conclusions: This unique study noted racial differences in acute toxicity in women with breast cancer who were treated with FEC 100 chemotherapy. However, there are several limitations including the retrospective nature of our study, differences in practice across four countries, and different number of patients available for comparison. This study is ongoing and further statistical analyses are planned when a larger sample size is reached. [Table: see text] No significant financial relationships to disclose.
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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 | 0.002 |
| 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) | 0.002 | 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".