Development and implementation of “advanced cancer shared care letters” to improve shared care between oncologists and family physicians.
Bibliographic record
Abstract
37 Background: Optimal care of patients living with advanced cancer requires a collaborative approach between oncologists and family physicians (FPs), starting early in the disease trajectory. We developed and implemented “advanced cancer shared care letters” to improve communication, collaboration and role clarity amongst providers. Methods: A physician-to-physician standardized “advanced cancer shared care letter” for colorectal cancer was created at a Canadian tertiary cancer center with input across stakeholders. The letter is ordered by the oncologist when they determine a patient to have advanced (i.e. incurable) cancer. The letter outlines components of shared care, division of responsibilities, monitoring for complications, responding to oncological emergencies, and consultation services such as palliative care. A cover sheet is provided for FPs to return to confirm their involvement, indicate their comfort level with providing a palliative approach to care (e.g. advance care planning, managing symptoms) and ask questions. Letters were piloted in two gastrointestinal (GI) oncology outpatient clinics for two months, and then implemented in the seven remaining GI clinics over two months. Metrics were collected for five months to evaluate implementation. Results: Over 5 months, 76 shared care letters were ordered; in 5 cases, no FP was identifiable. Cover sheets were returned by 39/71 FPs (55%). Content returned included prognosis questions, goals of care conversations, supportive services available in their practice and those in use by the patient, capacity to manage symptoms (e.g. opioid prescribing), and requests to engage palliative care services. Implementation challenges included frequent change in clerical staff and management, electronic chart challenges and variable adoption. Conclusions: The shared care letter provides a useful mechanism for FPs and oncologists to share information. It increases communication and care coordination between typically siloed providers, to enhance patient experience. A similar letter is provided to patients and we are now developing a shared care letter that is generalizable for any type of advanced cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".