Patterns of Survivorship Follow-Up Care Among Patients With Breast Cancer: A Retrospective Population-Based Cohort Study in Ontario, Canada, Between 2006 and 2016
Bibliographic record
Abstract
PURPOSE: Many cancer survivors have ongoing follow-up with their oncologist(s), despite evidence that this care can be competently managed by primary care and transitioning well survivors could relieve growing pressure on cancer care systems. We analyzed population-based administrative data from Ontario, Canada, to examine rates of transition to primary care-led follow-up care during the survivorship phase, including clinical and demographic predictors associated with being transitioned. METHODS: We conducted a retrospective cohort study to describe the patterns of survivorship follow-up care among all patients with breast cancer in Ontario from 2006 to 2016. Data were derived from the Ontario Cancer Registry and other linked data sets. We defined the survivorship phase of care beginning at 2 years after initial diagnosis. Logistic regression was used to explore factors potentially prognostic of no oncology visits in each of the years after survivorship. RESULTS: Our survivorship cohort was composed of 71,719 patients with breast cancer, 42% of whom were considered to have transitioned from oncology to primary care 2 years after diagnosis. Although the number of patients having oncology visits diminished over time, a quarter of the cohort continued being seen in year 5 of survivorship. Regression analysis found older age, early cancer stage, living farther from a cancer center, not receiving radiation or chemotherapy, and high well-being to be associated with transitioning to primary care. CONCLUSION: Our findings contribute to the development of low-risk profiles among survivors to inform optimal transition from oncology to primary care. Further research examining qualitative perspectives from oncologists, cancer survivors, and primary care is also required to illuminate other sentinel factors to be considered when transitioning during follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".