The impact of new chemotherapeutic and hormone agents on survival in a population‐based cohort of women with metastatic breast cancer
Bibliographic record
Abstract
BACKGROUND: Over the past decade, a number of new therapeutic agents have become available in the treatment of metastatic breast cancer (MBC). This study characterized the use and assessed the impact on survival of population-based access to new agents for the treatment of MBC. METHODS: The dates of release in British Columbia of 7 new systemic agents for MBC during the 1990s were used to construct 4 time cohorts. All patients with a first diagnosis of distant metastases in each of the time cohorts were identified and characterized, and their survival was compared. Cox proportional regression modeling was used to assess for predictors of survival. RESULTS: In total, 2150 patients with a first distant metastases diagnosed during 1 of the 4 cohort intervals were identified. Baseline characteristics between cohorts were similar, except a greater proportion of the later cohorts received adjuvant chemotherapy (P < .001), had positive estrogen receptor status (P = .01), and had a longer median time from initial diagnosis to MBC (P < .001). Survival in Cohort 1 (1991-1992) and Cohort 2 (1994-1995; median, 438 days and 450 days, respectively) was similar. Survival was longer in Cohort 3 (1997-1998; median, 564 days; P = .002) and improved further in Cohort 4 (1999-2001; median, 667 days; P = .05). In multivariate analysis, the later cohorts were associated independently with improved survival (P = .01 and P < .001, respectively). CONCLUSIONS: Population-based access to new therapeutic agents for MBC appeared to be associated with improved survival. To the authors' knowledge, this is the first study to date that demonstrates, from a population-based perspective, improving survival over the past decade for women with MBC.
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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 | 0.001 | 0.003 |
| 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".