Identifying critical data elements for a provincial transition plan from a primary care perspective.
Bibliographic record
Abstract
54 Background: Cancer Care Ontario is the provincial government advisor on the cancer and renal systems, as well as access to care for key health services. Enhancing flow of patient information and care plans are priorities to improve continuity and quality of survivorship care. Cancer Care Ontario’s Primary and Community Care and Survivorship Programs have initiated work to identify required information that primary care providers (PCPs) should receive about a patient’s cancer care at the point of transition back to primary care. Methods: Fifteen focus groups were conducted with PCPs in Ontario using a guide to facilitate group discussions on the utility and content of transition plans. Based on the collective feedback, thematic analysis was conducted on data elements that were expressed as critical with 12 common themes identified. Transition plan materials across Ontario’s Local Health Integration Networks and relevant jurisdictions in Canada and the United States were also reviewed to abstract a list of all documented data elements. A comprehensive matrix of data elements was then created by incorporating the list of all documented data elements with the 12 common themes. Using this matrix, prevalence of data elements amongst reviewed materials was ranked by frequency. A Modified-Delphi approach was used to validate and prioritize data elements with Cancer Care Ontario provincial and regional primary care clinical leadership. Results: In total, 21 documents were reviewed and 30 standard data elements were identified and ranked by frequency. The 10 most frequent data elements were classified as required for a standard transition plan. The remaining data elements were presented to 29 Cancer Care Ontario Cancer Leads to reach consensus on a core set of data elements to be required for inclusion in a transition plan. Conclusions: Essential data elements for inclusion in a transition plan have been identified from the perspective of PCPs. Next steps include engaging patient and family advisors, oncologists, and health system administrators through a phased regional consultation process. The role of synoptic reporting for a future standard survivorship transition plan will also be explored.
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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 | 0.048 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".