Patterns and Predictors of First-Line Taxane Use in Patients with Metastatic Triple-Negative Breast Cancer in US Clinical Practice
Bibliographic record
Abstract
We investigated first-line (1L) treatment patterns and predictors of taxane use to better understand the evolving metastatic triple-negative breast cancer (mTNBC) treatment landscape. This retrospective analysis of the Truven Health MarketScan® (Somers, NY, USA) Database included women with mTNBC who received 1L therapy within six months of diagnosis (January 2005–June 2015). Multivariate logistic regression models identified predictors of taxane use, adjusting for prognostic factors. A total of 2271 women with newly diagnosed mTNBC received 1L treatment during the study period. Half received a 1L taxane (53%), more often in combination than as monotherapy (58% versus 42%), though this varied by specific taxane. Nab-Paclitaxel monotherapy increased substantially after 2010. More recent treatment year (odds ratio, 2.16 (95% CI 1.69–2.76]) and number of metastases (≥3 versus 1: 1.73 (1.25–2.40)) predicted taxane monotherapy versus combination. Having a health maintenance organization versus a preferred provider organization plan predicted less nab-paclitaxel versus paclitaxel (0.32 (0.13–0.80)) or docetaxel (0.30 (0.10–0.89)) use. More recent index year (2011–2015 vs. 2005–2010) was the only predictor favoring nab-paclitaxel versus paclitaxel (2.01 (1.26–3.21)) or docetaxel (3.63 (2.11–6.26)). Taxane-containing regimens remained the most common 1L mTNBC treatments. Paclitaxel and nab-paclitaxel use changed substantially over time, with nab-paclitaxel use associated with insurance coverage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".