Multidisciplinary cancer conferences as a forum for early identification of patients who can benefit from palliative care.
Bibliographic record
Abstract
148 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 can benefit from palliative care using the UK Gold Standards Framework Surprise Question (would you be surprised if this person died within the next year?). Multidisciplinary cancer conferences (MCCs) are scheduled meetings for oncology teams to prospectively discuss patient diagnostic tests and treatment options, which were leveraged for the INTEGRATE Project. Methods: A pilot study to test the efficacy of the Surprise Question at MCCs and implementation of a palliative model of care has been implemented in 3 academic and 1 non-academic cancer centres. A survey was completed to identify provider comfort levels in providing palliative care. All sites completed Pallium Canada’s LEAP Onco module, which equips providers with skills to provide primary level palliative care. Patients identified at MCCs received advance care planning, symptom management, referrals and standardized reporting to primary care. Results: Four disease sites (Lung, GI, CNS and Head & Neck) are participating. A baseline survey showed over 50% of providers had no training in palliative care. 161 providers participated in LEAP Onco. Implementation of the Surprise Question at MCCs began in February 2015. Two months of implementation identified 39 patients at the CNS, Lung and GI disease sites out of 108 of patients reviewed. At the CNS MCC, 100% glioblastoma patients were identified. The Lung and GI disease sites had lower identification rates (27% and 10%, respectively). Identified patients will have their healthcare utilization (referrals to community providers, billing patterns, ER visits) analyzed to determine impact of this project. Implementation continues until 2016. Conclusions: MCCs appear to be an excellent forum for identifying patients who can benefit from a palliative approach to care within the CNS, Lung and GI site groups. Further data is being collected to inform provincial recommendations for broader implementation of palliative care 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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".