Transition of breast cancer (BC) survivors to primary care: Results of a Cancer Care Ontario (CCO) pilot project.
Bibliographic record
Abstract
58 Background: Emerging evidence indicates that the transition of well breast cancer survivors to primary care is safe and effective. Methods: Prospective longitudinal cohort study across 14 health regions in Ontario, Canada. Each region received $100 000 (CAN) to develop and implement a sustainable new model of survivorship care for BC survivors that involved transition from medical oncology-led practice. Each region had a designated lead, and support from primary care. Funding could be used to develop any aspect of the model including personnel support, development of communication materials and outcome measurement. A minimal dataset reporting requirement included a description of the program, documentation of transitioned BC survivors as well as standardized patient and provider experience feedback once transition completed. Results: All 14 health regions in Ontario participated and all developed a survivorship care plan and patient educational materials. The models developed included direct transition to primary care in 6 regions, a nurse-led transition clinic in 4 and a GP-led transition clinic in 4. To date, 3,418 BC survivors have transitioned. Of 676 BC respondents to date, 83% felt there was a clear plan for follow up and 87% felt adequately prepared for transition. Feedback from primary care providers demonstrated that many feel informed about intent of transition and understand their role in on-going follow-up care. Process outcomes such as re-referral back to cancer center and adherence to follow-up guidelines are currently underway. Conclusions: The wide scale transition of appropriate BC survivors to a primary setting appears feasible with high acceptability by patients and providers.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".