Developing and evaluating new models of care in hematology.
Bibliographic record
Abstract
e21557 Background: The journey of care for patients with hematological cancer from the time of diagnosis can often be five years or longer. During this period, patients frequently visit their hematology team and may not maintain regular appointments with their family doctor. In doing so, patients may leave other non cancer related health issues, unmonitored. As such, we plan to improve patient care by developing new models of care in hematology to address key medical and psychological needs across the trajectory of care. The goal is to facilitate transition to long term care to primary care where appropriate. Methods: Reviewed published literature on shared care and survivorship modeling for other cancers and included key stakeholder discussion. Conducted semi-structured focus groups with a total of 26 participants (patients, caregivers, family doctors) from the Greater Hamilton area in Ontario, Canada. Participants were asked to share their views on drafted models of care for people with hematological cancer. Results: Three models around the trajectory of care were developed (shared, survivorship and engagement). The analysis revealed that patients were willing to have their family doctor take on more routine follow up visits to monitor blood work as long as the results were readily shared with their hematologist. Patients agreed that more visits to their family doctor could only improve their overall health, although some were apprehensive about their family doctor’s abilities to care for them following their cancer diagnosis. Family doctors similarly were unsure how to treat these patients and felt that clear, concise guidelines need to be developed to outline how to properly care for the long term effects of cancer treatments. Conclusions: Many patients would be willing to engage in shared care with their family doctor during their cancer journey if family doctors were more confident in their cancer care abilities and if communication improved between specialty and primary care. Patients felt that shared care would be beneficial to their overall health. Family doctors stressed that care plans are concise and not outside of their scope. Overall, both family doctors and patients felt that the new models of care, once developed, will help to improve patient outcomes.
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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 | 0.057 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".