Adjustments in relative dose intensity (RDI) for FECD chemotherapy in breast cancer: A population analysis.
Bibliographic record
Abstract
547 Background: Reductions in RDI of adjuvant chemotherapy for breast cancer (BC) has been associated with inferior survival. However, earlier studies may be confounded by uncharacterized BC subtype(s) (TNBC, HER2+) and non-taxane chemotherapy regimens (CMF, AC). This retrospective study evaluates survival (DFS/OS) outcomes for patients receiving RDI reductions for FECD adjuvant chemotherapy in Alberta, Canada. Methods: Patients with stage I-III, ER +/-, HER2- BC receiving adjuvant FECD chemotherapy from 2007-2014 were identified using the Alberta Cancer Registry. RDI of individual chemotherapeutics (cycle 1-6) were recorded. Average RDI was stratified by <85% vs ≥85% of total dose. Subgroup analysis for early (cycle 1-3) versus late (cycle 4-6) RDI reductions were evaluated. Events (recurrence/death) from any cause were identified. Results: FECD patients (n=1304) receiving an average RDI <85% (range 25-84%) compared to ≥85% demonstrated a significant decline in DFS (79% vs 85%; p<0.01) and OS (82% vs 89%; p<0.01). Early reductions (any) compared to no reduction in RDI were correlated with inferior DFS (77% vs 86%; p<0.01) and OS (79% vs 90%; p<0.01). Late reductions in RDI did not affect DFS/OS. Proportions of TNBC were non-significant for comparative cohorts. Significantly more N0 and N1-3 patients were seen in the any and no early reduction cohort respectively. Conclusions: In high risk BC patients, average RDI <85% is correlated with reduced DFS/OS for FECD. Early (FEC) compared with late (docetaxel) reductions in RDI are correlated with inferior survival. This data suggests that where possible, total (<85%) and early (FEC) dose reductions should be avoided in patients receiving adjuvant FEC-D chemotherapy. [Table: see text]
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".