Adherence to Breast Cancer Follow-up Care Guidelines for Vulnerable Populations in four Canadian provinces: a CanIMPACT study
Bibliographic record
Abstract
IntroductionBreast cancer survivors are at risk for late and ongoing problems including cancer recurrence and late effects of treatment. Vulnerable groups may not enjoy equitable access to quality follow-up care. This study examines utilization of guideline-based follow-up care among vulnerable subpopulations in four Canadian provinces. Objectives and ApproachFor vulnerable groups of breast cancer survivors diagnosed from 2007-2010 in British Columbia (BC), 2007-2011 in Manitoba (MB), 2007-2010 in Ontario (ON), and 2007-2012 in Nova Scotia (NS), alive at 30 months post-diagnosis and followed for five years from diagnosis, we undertook a retrospective population-based cohort study linking cancer registries, clinical and health administrative databases. We calculated adherence to recommended follow-up care for surveillance of recurrent and new cancer, late effects, and general preventive care, and examined variation among provinces. Vulnerable groups were defined as those diagnosed at older ages, with lower income status, and/or who resided in rural area. ResultsSurvivor numbers were 23,700 (ON), 9493 (BC), 2688 (MB), and 2735 (NS). In Year 2, between 9.3% (BC) and 28.1% (ON) of survivors diagnosed aged 74+ years received annual breast cancer-related PCP or oncologist follow-up visits, a lower proportion than their younger-diagnosed counterparts; rates of surveillance breast imaging (between 34.2% (BC) and 68.6% (ON) in Year 2) were also lower than those diagnosed at younger ages. Those with incomes in the lowest 40\% did not have different rates of primary care physician and oncologist visits compared to the top 60%, nor did their utilization of surveillance imaging or imaging for metastatic disease differ. Guideline-adherent surveillance breast imaging was conducted on a higher proportion of urban than rural patients in all provinces. Conclusion/ImplicationsWhile area-level incomes do not appear to appreciably affect follow-up care, older age and rural residence resulted in differential access to care. These results suggest that there are gaps in provision of follow-up care that potentially can be addressed through system and practice-level change.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".