Evaluating the Necessity and Impact of Cardiac Imaging on Breast Cancer Care in Northwestern Ontario
Bibliographic record
Abstract
INTRODUCTION: Breast cancer affects over 25,000 women annually in Canada and has seen improved survival rates due to advances in screening and treatment. However, cardiotoxic therapies including anthracyclines and trastuzumab have made cardiovascular disease a leading cause of death among survivors. Baseline left ventricular ejection fraction is a reliable predictor of heart failure, and various guidelines recommend pretreatment cardiac imaging; however, its utility is largely based on expert opinion. METHODS: This retrospective cohort study analyzed 93 breast cancer patients treated at a single cancer centre in Northwestern Ontario between 2012 and 2017 to determine the yield (defined as imaging leading to clinically actionable changes in care) of imaging. RESULTS: = 30, mean age = 59.37 ± 10.91). Due to the very small sample size in cohort A, findings are presented for qualitative insight only. Cohort B had the highest imaging yield (13.33%), while cohorts A and C showed lower yields (7.14% and 4.17%) with more frequent imaging. Predictors of higher yield varied, with cohort B identifying the most, including diabetes and coronary artery disease. CONCLUSIONS: These findings underscore the need for targeted cardiac imaging to optimize resource allocation and patient outcomes, particularly in resource-limited settings such as Northwestern Ontario. Subsequent investigations should seek to stratify proactive versus reactive interventions, evaluate outcomes, refine imaging guidelines, and gather more data on patients receiving trastuzumab.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".