Multidisciplinary cancer conferences as a forum for early identification of patients who can benefit from a palliative approach to care: INTEGRATE quality improvement project.
Bibliographic record
Abstract
196 Background: Introducing palliative care early in the cancer journey results in a better quality of life, less aggressive care and longer survival compared to patients receiving standard care. The INTEGRATE project goal is to identify and manage patients who may benefit from palliative care earlier in their care. Multidisciplinary cancer conferences (MCCs) are scheduled meetings for oncology teams to prospectively discuss patient diagnostic tests and treatment options. In this study, we tested whether MCCs could be used as a forum to identify patients using the UK Gold Standards Framework Surprise Question (Would you be surprised if this person died within the next year?). Methods: Cancer centres volunteered to participate in a pilot project to test the efficacy of the Surprise Question at MCCs and implement a palliative model of care. Prior to initiation, a survey was completed to identify provider comfort in providing palliative care. All sites received primary level palliative care education by completing Pallium Canada’s LEAP Oncology module. Identified patients received Advance Care Planning, symptom management, referrals and standardized reporting to primary care. Results: A baseline survey showed over 50% of providers had no palliative care training. 157 providers participated in LEAP Oncology. Three MCC disease sites (Lung, GI and CNS) are participating in 3 academic and 1 non-academic cancer centres. Implementation of the Surprise Question began in February 2015 and by August 2016, 300 patients were identified out of 1086 total reviewed patients. At the CNS MCC, 100% of all glioblastoma patients were identified. The Lung and GI sites had lower identification rates (19% and 19%, respectively). Identified patients will have their healthcare utilization (community referrals, billing patterns, etc.) analyzed and final results will be synthesized by project completion in January 2017. Conclusions: MCCs appear to be an excellent forum for identifying patients who may benefit from a palliative approach to care within CNS, Lung and GI site groups. Further analysis will inform broader palliative care recommendations in Ontario.
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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 | 0.079 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".