Population-level impact of adjuvant trastuzumab emtansine on the incidence of metastatic breast cancer: an epidemiological prediction model of women with HER2-positive early breast cancer and residual disease following neoadjuvant therapy
Bibliographic record
Abstract
PURPOSE: Treating early-stage breast cancer (eBC) may delay or prevent subsequent metastatic breast cancer (mBC). In the phase 3 KATHERINE study, women with human epidermal growth factor receptor 2 (HER2)-positive eBC with residual disease following neoadjuvant therapy containing trastuzumab and a taxane experienced 50% reductions in disease recurrence or death when treated with adjuvant trastuzumab emtansine (T-DM1) vs adjuvant trastuzumab. We predicted the population-level impact of adjuvant T-DM1 on mBC occurrence in five European countries (EU5) and Canada from 2021-2030. METHODS: An epidemiological prediction model using data from national cancer registries, observational studies, and clinical trials was developed. Assuming 80% population-level uptake of adjuvant treatment, KATHERINE data were extrapolated prospectively to model projections. Robustness was evaluated in alternative scenarios. RESULTS: We projected an eligible population of 116,335 women in Canada and the EU5 who may be diagnosed with HER2-positive eBC and have residual disease following neoadjuvant therapy from 2021-2030. In EU5, the cumulative number of women projected to experience relapsed mBC over the 10-year study period was 36,009 vs 27,143 under adjuvant trastuzumab vs T-DM1, a difference of 8,866 women, equivalent to 25% fewer cases with the use of adjuvant T-DM1 in EU5 countries from 2021-2030. Findings were similar for Canada. CONCLUSION: Our models predicted greater reductions in the occurrence of relapsed mBC with adjuvant T-DM1 vs trastuzumab in the indicated populations in EU5 and Canada. Introduction of T-DM1 has the potential to reduce population-level disease burden of HER2-positive mBC in the geographies studied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".