2020The spectrum of cardiovascular disease after early stage breast cancer: a population-based cohort study
Bibliographic record
Abstract
Background: There is increasing interest in heart failure (HF) after early stage breast cancer (ESBC). Rates of other forms of cardiovascular disease (CVD) and their temporal relationship to HF are unknown. Purpose: To describe the spectrum of CVD after ESBC, with comparison to age-matched controls. Methods: We used the Ontario Cancer registry to identify 124733 women diagnosed with ESBC between April 1 1998 and March 31 2015. Clinical data were obtained by linkage to administrative databases. Women were matched 1:3 on birth year to 374199 controls who were alive without cancer history on the index date. We identified all hospital presentations for CVD through available follow-up. Cumulative incidence function curves were used to estimate CVD incidence, with all-cause death treated as a competing risk. This was also done separately for HF, ischemic heart disease (IHD), cerebrovascular disease, and arrhythmias. Cause-specific regression models were used to compare the hazard of CVD between event-free women and age-matched controls. We tested the proportional hazards assumption for the ESBC indicator variable by allowing the natural logarithm of the HR to change linearly over time. The time-interaction term was significant, so HRs are presented at 1 and 5 years. Analyses were repeated after dividing the ESBC cohort based on chemotherapy receipt.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".