Effect of Capecitabine on Mean Corpuscular Volume in Patients With Metastatic Breast Cancer
Bibliographic record
Abstract
Capecitabine is a novel oral chemotherapy agent designed to generate 5-fluorouracil (5-FU) preferentially in tumor tissue, and is the most effective therapy for anthracycline and taxane-resistant breast cancer. Macrocytosis has not been previously reported in association with capecitabine therapy. We performed a retrospective review of consecutive metastatic breast cancer (MBC) patients receiving standard 21-day cycles of oral capecitabine therapy at a single center during the year 2000. Patients were assessed prior to each cycle with clinical examinations and complete blood counts. Seventy-six women (median age 52 years, median follow-up 273 days) met inclusion criteria for the study. Prior to treatment, the average mean corpuscular volume (MCV) was 91.6 fl (normal range 80-100 fl). During chemotherapy, MCV increased in a dose-dependent and time-dependent manner. Fifty-seven percent of study patients developed macrocytosis (MCV > 100 fl) while on capecitabine therapy; 85% of women who received at least nine cycles of therapy exhibited macrocytosis. Development of macrocytosis was independent of anemia, thrombocytopenia, neutropenia, liver metastasis, and hepatic dysfunction; however, increases in MCV were more pronounced in 5-FU-naive patients. Alternative causes of macrocytosis were not identified in patients without coexisting anemia. We conclude that capecitabine therapy produces time-dependent and dose-dependent macrocytosis in MBC patients. However, macrocytosis was not associated with anemia or overt myelosuppression. When capecitabine-treated breast cancer patients develop macrocytosis in the absence of anemia, investigations of other causes of macrocytosis are not warranted.
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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 | 0.004 |
| 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) | 0.000 | 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".