Treatment patterns and characteristics of post-menopausal women with HR+/HER2- metastatic breast cancer receiving everolimus.
Bibliographic record
Abstract
e11502 Background: The use of everolimus (EVE) in combination with exemestane in postmenopausal women with HR+/HER2- metastatic breast cancer (mBC) has been studied in clinical trials, yet little is known of its real-world utilization. We aimed to characterize treatment patterns and characteristics of HR+/HER2- mBC patients (pts) with first-line endocrine therapy (Tx), who ever vs. never used EVE in real-world settings. Methods: Data were from a community oncology electronic medical records database from Altos Solutions, Inc. Eligible pts were postmenopausal women, with ≥ 1 medical record with a BC diagnosis, confirmed HR+/HER2- status, first mBC diagnosis after July 1, 2012, and received first-line endocrine Tx. Characteristics of pts who ever vs. never received EVE were compared. Potential predictors of EVE use, including age at index date and at first breast cancer diagnosis, race, region, insurance type, metastatic sites, ECOG performance status, and disease recurrence, were assessed using logistic regressions. Results: Of the 676 pts who met the inclusion criteria, 83 (12%) ever received EVE vs. 593 (88%) never. EVE was initiated primarily in second line (42.2%), followed by first line (41.0%), third line (10.8%), and fourth + lines (6%). Pts receiving EVE were younger at first mBC diagnosis (mean, 62 vs. 69 years; p < 0.001) and at first BC diagnosis (58 vs. 66 years; p < 0.001). Distribution of ECOG was similar between the 2 cohorts. Controlling for other characteristics, age was a significant predictor of EVE use in first-line Tx (odds ratio: 0.95, p = 0.003), but not in later lines. While not statistically significant, pts with higher ECOG were more likely to receive EVE in first line, and not in later lines. Pts with bone metastases were less likely to receive EVE in first line, but more likely in second + lines. Insurance type was not a predictor of EVE use. Conclusions: EVE is typically used in second line in pts with HR+/HER2- mBC with first-line endocrine Tx. Pts who start EVE earlier tend to be younger with worse prognosis for mBC. Studies with larger sample sizes are needed to further characterize EVE pts and assess their treatment outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".