Abstract P5-14-11: An estimation of the population survival benefit of first-line chemotherapy and immunotherapy for breast cancer
Bibliographic record
Abstract
Abstract Purpose:Randomized clinical trials describe the benefit of chemo-and immunotherapy for specific breast cancer patients with selected patient and disease characteristics. However, variability in practice occurs despite evidence-based guidelines [1]. The overall survival benefit for the whole population of breast cancer patients in Australia, if evidence-based guidelines for chemo-and immunotherapy were implemented, is unknown. Our study's purpose was to estimate the overall population survival benefit of routinely using evidence-based practice. Methods and Materials:Decision trees with evidence-based indications for chemotherapy have been previously defined [2]. Each branch corresponds to a specific cohort who have, or do not have, defined indications for chemotherapy and/or immunotherapy. Chemo -and immunotherapy benefit was defined as the absolute incremental benefit of either chemotherapy and/or immunotherapy over no chemo- and/or immunotherapy for radical and palliative indications. Multiple electronic citation databases were systematically queried, including Medline and the Cochrane Library. In cases where there were multiple sources of the same level of evidence, hierarchical meta-analysis was performed. The benefits of chemo- and immunotherapy were estimated for 1, 5, 10-year survival. To assess the robustness of our estimates, sensitivity analyses were performed. Results: The estimated 1-year, 5-year and 10-year absolute population-based overall survival benefits of optimally utilized chemo- and immunotherapy for breast cancer in Australia are 1.0% (95% CI, 0.9%-1.2%), 4.4% (95% CI, 4.3%-4.6%) and 5.2% (%-%), respectively. They are summarized in the Table 1. Estimation of Population Survival Benefit for First Line Chemo- and Immuno TherapyBreast CancerProportion of all cancer in Australia1 year survival benefit (Sensitivity range)5 year survival benefit (Sensitivity range)10 year survival benefit (Sensitivity range)Stage I-II10.0%0.6% (0.6%-0.7%)4.8% (4.6%-5.0%)6.9% (6.7%- 7.2%)Stage III1.6%3.0% (3.0%-3.1%)6.1% (5.8%-6.3%)0%Stage IV0.5%5.3% (5.1%-5.5%)4.9% (4.7%-5.1%)0%Whole Breast Cancer population12.1%1.0% (0.9%-1.2)4.4% (4.3%-4.6%)5.2% (5.0%-5.4%) Conclusion: Chemo- and immunotherapy agents improves overall survival in breast cancer at 1-, 5- and 10-years. Chemo-and immunotherapy provides a modest survival benefit to this patient population in Australia when it is used in accordance with guideline recommendations. These outcomes may allow comparison of treatment outcomes in a jurisdiction against what would be considered optimal based on evidence. 1. Fong, A., et al., A comparison of systemic breast cancer therapy utilization in Canada (British Columbia), Scotland (Dundee), and Australia (Western Australia) with models of "optimal" therapy. Breast, 2012. 21(4): p. 562-9. 2. Ng, W., Estimating the optimal chemotherapy utilisation rate as an evidence-based benchmark in cancers of the breast, upper gastrointestinal tract, gynaecological tract, head and neck, kidney, bladder, thyroid and unknown primary., in University of NSW, Faculty of Medicine. 2010, UNSW: Sydney. Citation Format: Delaney GP, Do V, Ng W, Barton MB. An estimation of the population survival benefit of first-line chemotherapy and immunotherapy for breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-14-11.
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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 | 0.049 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".