Moving forward after cancer: Implementation of transition appointments and follow-up care plans in Manitoba.
Bibliographic record
Abstract
50 Background: Patient experience and local and international clinical research have focused attention on transitions in cancer care from treatment to follow-up. Deficiencies in the quality of survivorship care, including necessary testing, have been demonstrated. The Canadian Partnership Against Cancer has funded projects designed to enhance the implementation of survivorship care plans (SCPs) and to improve cancer system-primary care coordination. CancerCare Manitoba, a provincial agency, is a leader in this work. Methods: The Moving Forward After Cancer initiative combines the generation of standardized written SCPs from the EMR with enhanced patient assessment at the end of curative systemic / radiation therapy. A transition appointment (TA) is provided by the patient’s usual oncology providers and includes screening for distress, appropriate referrals, and provision of a personalized treatment summary and SCP to the patient, with copies to the primary care physician and surgeon. The TA often marks the transfer of medical responsibility to the primary care provider (PCP). Results: Transition appointments have been implemented for colorectal (2012), breast (2014), and lymphoma and gynecologic cancer patients (2015). A total of 364 TAs were done in Manitoba in 2014, of which 140 were with colorectal (stage II and III) and 224 with breast (stage I – III) patients, about 59% and 35% respectively of eligible patients. This is an increase of 385% from the 75 TAs done in 2013. Other patient outcomes being collected include perceptions of continuity of care, confidence in survivorship information, evaluation of the care plan documents and also PCP and oncology team perceptions. In order to support practice change, a workflow solution led by a designated team that is adaptable by all sites across the province has been developed. Conclusions: The provision of TAs and SCPs is being well adopted in Manitoba. We expect that this intervention willimprove the experience of both patients and health care providers and the quality of care at the time of transition to survivorship. Implementation is underway with other disease site groups with the goal of all patients receiving a TA as they transition into survivorship.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".