241 Optimizing the transition of cancer survivorship care from oncologists to primary care providers (PCP)
Bibliographic record
Abstract
Objectives/Goals: Aims are to identify the gaps and discrepancies between cancer care teams at Princess Margaret (PM) and primary care providers (PCPs). To ensure the transition from hospital care at PM into the community integrates the expressed needs of PCPs and cancer specialists. To ensure PCPs have the necessary resources to provide high-quality care to patients. Methods/Study Population: Phase 1 is the preparation phase, which consists of searching the literature and conducting contextual inquiry with experts in relevant fields, such as cancer survivorship and primary care. This phase is crucial to the planning of this project as the information gathered will be used to define the problem space and outline the scope of the project. Next (phases 2 and 3) we aim to create and distribute surveys to PCPs to gather data on current protocols and resources. We plan to distribute this survey by emailing PCPs and accessing PCP networks. Upon completion of the survey, we will review the data and assess which areas need further investigation. Then, we will create an interview guide keeping in mind the areas that need to be supplemented and aiming to validate the need. Results/Anticipated Results: A resource that presents guidelines for PCPs to assist in them taking on follow-up care responsibilities for low-risk cancer survivorship patients. These guidelines may include information such as communication pathways between PCPs and the PM Cancer Care team, expected follow-up care measures, and timeframes for follow-up care. The development of this guideline will assist in alleviating the burden on the PM Cancer Centre system as it will facilitate low-risk patients transitioning back to family care. Discussion/Significance of Impact: There is an increasing demand for oncology services post-cancer treatment at the PM Cancer Centre and the current cancer model follow-up care is not sustainable by oncologists alone. There is a need to explore innovative personalized pathways to follow-up care based on an individual’s needs and integrate family doctors.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".