A retrospective analysis of changes in distant and breast cancer related disease-free survival events in adjuvant breast cancer trials over time
Bibliographic record
Abstract
Disease-free survival (DFS) comprises both breast cancer and non-breast cancer events. DFS has not been validated as a surrogate endpoint for overall survival (OS) in most breast cancer subtypes. We assessed changes to the type of events contributing to DFS over time. We identified adjuvant studies in breast cancer (BC) from 2000 to 2020 where the endpoint was DFS. We examined change in distant DFS events and the BC-related DFS using univariable and multivariable linear regression. Data were reported quantitatively using the Burnand criteria irrespective of statistical significance. We included 84 studies (88 cohorts), comprising 212,191 participants, 41,604 DFS events and 23,205 distant DFS events. The DFS event rate/100 participants/year has declined modestly over time (ß - 0.34, p = 0.001). Start year was negatively associated with distant DFS events (ß - 0.58, p < 0.0001); however, the effect was lost after adjusting for follow-up time (ß - 0.18, p = 0.096). The average number of BC-related events/100 participants/year also declined over time (ß - 0.28, p = 0.009). In multivariable analysis, start year and ER expression were quantitatively associated with distant DFS events and BC-related DFS events. DFS events have declined over time driven by a reduction in BC related events. As DFS events are increasingly defined by non-BC events, there will be limited surrogacy between DFS and OS.
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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 | 0.046 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".