Developing a prediction model for benefit from fulvestrant in heavily pretreated metastatic breast cancer (MBC) patients
Bibliographic record
Abstract
1041 Background: Fulvestrant use in heavily pretreated patients with MBC is associated with highly variable responses. This study aimed to characterize the benefit of fulvestrant therapy and develop a prediction model for clinical benefit in this setting. Methods: A nationwide, retrospective chart review of patients enrolled in a Canadian compassionate use program was performed. This program mandated prior therapy with tamoxifen and both steroidal and non-steroidal aromatase inhibitors. Charts from the seven highest accruing centers were reviewed. Sample size was based on the derivation of a model to predict the probability of a patient remaining on fulvestrant and free from chemotherapy for at least 3 months. Results: 305 women received at least one dose of fulvestrant; 207 went on to receive chemotherapy (68%). Of these, 48 (23%) required chemotherapy at 3 months, 113 (55%) at 6 months, and 170 (82%) by 12 months. Median duration of fulvestrant treatment was 126 days (range 23–1920). Median overall survival from start of fulvestrant was 698 days (25 th percentile 316 days-75 th percentile 1,359 days). The preliminary prediction model showed that older age (OR 0.96, 95% CI 0.93–0.99) and having received no adjuvant hormonal therapy (OR 0.5, 95% CI 0.2–1.25) predicted a greater chance of remaining chemotherapy-free at 3 months. Presence of lung (OR 2.55, 95% CI 1.1–5.9) or brain metastases (OR12.8, 95% CI 4.1–55.4) predicted a lower chance of remaining chemotherapy-free at 3 months. Conclusions: Older age and having received no prior adjuvant hormonal therapy predicted a greater chance of remaining chemotherapy free at 3 months, while lung and brain metastases predicted a lower chance. These factors will be validated in an international data set, and may be considered when prescribing fulvestrant. A 6-month prediction model is currently under development. [Table: see text]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".