Real-World Experience with Adjuvant FEC-D Chemotherapy in Four Ontario Regional Cancer Centres
Bibliographic record
Abstract
BACKGROUND: The efficacy of adjuvant chemotherapy with fec-d (5-fluorouracil-epirubicin-cyclophosphamide followed by docetaxel) is superior to that with fec-100 alone in women with early-stage breast cancer. As the use of fec-d increased in clinical practice, health care providers anecdotally noted higher-than-expected toxicity rates and frequent early treatment discontinuations because of toxicity. In the present study, we compared the rates of serious adverse events in patients who received adjuvant fec-d chemotherapy in routine clinical practice with the rates reported in the pacs-01 trial. METHODS: We retrospectively reviewed all patients prescribed adjuvant fec-d for early-stage breast cancer at 4 regional cancer centres in Ontario. Information was collected from electronic and paper charts by a physician investigator from each centre. Data were analyzed using chi-square tests, independent samples t-tests, one-way analysis of variance, and univariate regression. RESULTS: The 671 electronic and paper patient records reviewed showed a median patient age of 52.2 years, 229 patients (34.1%) with N0 disease, 508 patients (75.7%) with estrogen or progesterone receptor-positive disease (or both), and 113 patients (26%) with her2/neu-overexpressing breast cancer. Febrile neutropenia occurred in 152 patients (22.7%), most frequently at cycle 4, coincident with the initiation of docetaxel [78/152 (51.3%)]. Primary prophylaxis with hematopoietic growth factor support was used in 235 patients (35%), and the rate of febrile neutropenia was significantly lower in those who received prophylaxis than in those who did not [15/235 (6.4%) vs. 137/436 (31.4%); p < 0.001; risk ratio: 0.20]. CONCLUSIONS: In routine clinical practice, treatment with fec-d is associated with a higher-than-expected rate of febrile neutropenia, in light of which, primary prophylaxis with growth factor should be considered, per international guidelines. Adoption based on clinical trial reports of new therapies into mainstream practice must be done carefully and with scrutiny.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".