PS1 - 163 Engaging in Difficult Discussions with Neuro-Oncology Patients: A Case Conference Based Quality Improvement Initiative
Bibliographic record
Abstract
Due to the non-curative nature of high grade brain tumours “difficult discussions” about goals of care, advance care planning, palliative care, and end of life are inherent to the practice of the neuro-oncology team. Clinician and patient barriers are common and may include anxiety about destroying hope, lack of readiness to discuss end of life topics, difficulty managing emotional responses, and concerns over competency in facilitating difficult discussions. Nonetheless, clinician comfort and skill in facilitating these discussions is critical and can impact patient perception of illness, coping response, and ability to make decisions about care. Methods At our regional cancer centre, a novel quality improvement initiative was designed to bring monthly case presentation of “difficult discussions” into a pre-existing weekly multi-disciplinary case conference (MCC). We will describe the rationale and developmental processes behind this initiative. Roughly 15 neuro-oncology clinicians attended each case conference, with guest attendance from palliative care and psychosocial oncology. Clinician groups represented included physicians (77%), nurses (8%), nurse practitioners (10 %), and occupational therapists (5%). Baseline and monthly surveys were administered to determine clinicians’ self- rated practices, skills, and attitudes towards “difficult discussions”. Results Early findings indicate that the initiative has been well-received. Physicians indicated highest levels of agreement with the statement “I feel that having difficult discussions is part of my responsibility”. Non physician groups indicated the greatest agreement that the intervention is beneficial to their practice. Discussion: Our challenges and successes may help guide others to incorporate a similar initiative at disease-site meetings.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".