Optimizing Cancer Survivorship Care: Examination of Factors Associated with Transition to Primary Care
Bibliographic record
Abstract
Healthcare systems in Canada and elsewhere have identified the need to develop methods to effectively and safely transition appropriate cancer survivors to primary care. It is generally accepted that survivors with a low risk of adverse events, including recurrence and toxicity, should be more systematically identified and offered transition. There remains a lack of clarity about what constitutes an appropriate profile that would assist greater application in practice. To address this gap, we examined the clinical profiles of patients that were transitioned from a large regional cancer centre to the community. The factors examined included disease site, clinical stage, time since diagnosis/first consult, cancer treatments, and Edmonton Symptom Assessment System (ESAS) scores. In total, 2604 patients were identified as transitioned between 2013 and 2020. These patients tended to have common cancers (e.g., breast, endometrium, colorectal) that were generally of lower stage. Half of the patients had received chemotherapy and/or radiation treatment. Nearly one-third of survivors were transitioned within a year of first consult and a third after five years. Most patients reported minimal symptoms based on ESAS scores prior to being transitioned. This study represents one of the first to analyze the types of cancer patients that are being selected for transition to primary 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".