Primary tumor and patient characteristics in breast cancer as predictors of adjuvant chemotherapy regimen: A regression model
Bibliographic record
Abstract
e11632 Background: Adjuvant chemotherapy is used to reduce the risk of recurrence of breast cancer. This study was undertaken to determine which patient and tumor characteristics are important in guiding the choice of adjuvant chemotherapy. Methods: A retrospective review was undertaken of patients diagnosed with breast cancer (stages I-III) at a regional cancer center from 2004–7. Patient and tumor characteristics were identified and chemotherapy regimens compared. Binary logistic regression analysis was performed to the choice of FEC/D, CEF, AC/T, or ddAC/T against AC or CMF, or the choice of chemotherapy to hormonal therapy only. Univariate analysis was used to select factors (p<0.1) for entry into a multivariate stepwise logistic regression model using the forward method. Odds ratios with 95% CI were calculated. A p-value of < 0.05 was significant and comparisons were two tailed. Results: Model 1 (n=871) included regimen (AC or CMF vs. aggressive regimen) as the dependant variable. Indicators of choice of aggressive regimen were higher stage [OR 4.7 (CI 3.3, 6.8)], positive nodes [2.5 (1.6, 3.8)], negative PR [2.1 (1.4, 3.1)], higher grade [1.4 (1.0, 1.8)], and age [0.91 (0.88, 0.92)]. Model 2 (n=640) involved choice of any regimen (chemotherapy vs. hormonal therapy only) as the dependant variable. Indicators of choice of chemotherapy were higher stage [7.19 (2.8, 18.4)], higher grade [7.02 (3.3, 14.8)], positive nodes [3.25 (0.98, 10.77)], age [0.85 (0.81, 0.90)], and ER negativity [0.04 (0.004, 0.37)]. Factors not significant in both models were: family history, comorbidities (renal/hepatic dysfunction, diabetes, cardiac history, or neuropathy), treating medical oncologist, histology, Her2/neu, > 3 positive nodes, ratio of positive to total nodes, multicentricity, multifocality, and positive margin status. Conclusions: This study verifies known important factors for choice of chemotherapy regimen as found in current guidelines, quantifies their effects at our center, and excludes others thought to be important. Further studies are required to confirm these results both nationally and internationally, where risk stratification may be different, and if variables predicting adjuvant radiation therapy are similar. 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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".