Use of Physician Services during the Survivorship Phase: A Multi-Province Study of Women Diagnosed with Breast Cancer
Bibliographic record
Abstract
INTRODUCTION: Oncologists have traditionally been responsible for providing routine follow-up care for cancer survivors; in recent years, however, primary care providers (pcps) are taking a greater role in care during the follow-up period. In the present study, we used a longitudinal multi-province retrospective cohort study to examine how primary care and specialist care intersect in the delivery of breast cancer follow-up care. METHODS: Various databases (registry, clinical, and administrative) were linked in each of four provinces: British Columbia, Manitoba, Ontario, and Nova Scotia. Population-based cohorts of breast cancer survivors were identified in each province. Physician visits were identified using billings or claims data and were classified as visits to primary care (total, breast cancer-specific, and other), oncology (medical oncology, radiation oncology, and surgery), and other specialties. The mean numbers of visits by physician type and specialty, or by combinations thereof, were examined. The mean numbers of visits for each follow-up year were also examined by physician type. RESULTS: The results showed that many women (>64%) in each province received care from both primary care and oncology providers during the follow-up period. The mean number of breast cancer-specific visits to primary care and visits to oncology declined with each follow-up year. Interprovincial variations were observed, with greater surgeon follow-up in Nova Scotia and greater primary care follow-up in British Columbia. Provincial differences could reflect variations in policies and recommendations, relevant initiatives, and resources or infrastructure to support pcp-led follow-up care. CONCLUSIONS: Optimizing the role of pcps in breast cancer follow-up care might require strategies to change attitudes about pcp-led follow-up and to better support pcps in providing survivorship care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".