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 (25th percentile 316 days-75th 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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".