A collaborative engagement of advance care planning within a diverse chronic disease population
Bibliographic record
Abstract
Patients living with Chronic Kidney Disease (CKD) have a high symptom burden associated with multi-co morbidities including diabetes and cardiovascular disease. The annual mortality rate of CKD patients is 25%, most of whom have not had the opportunity to engage in Advance Care Planning (ACP). This reality spurred on the British Columbia Provincial Renal Agency (BCPRA) to engage dedicated renal leaders from within BC to develop an ‘End of Life Framework’ for renal patients. This framework includes guidelines and recommended steps to address ACP with renal patients within B.C.'s 5 geographic Health Authorities (HA). We will describe the collaborative development of this framework and the subsequent implementation of a systematic, interdisciplinary approach to ACP within a culturally diverse 275 patient, haemodialysis unit at St. Paul's Hospital, Vancouver. We will share the successes as well the obstacles and mitigation strategies of implementation, from initial planning, program engagement, comprehensive staff education through to a pilot using the My Voice document and evaluation. We will share how ACP protocols are now embedded in the practice of a core interdisciplinary ACP team. First year audit results that indicate a marked improvement to a minimum 40% of haemodialysis patients having documentation of ACP conversations will be discussed. We will describe how the learnings from the ACP implementation at this renal unit are being shared and applied in diverse renal settings across this province.
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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 | 0.037 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.037 |
| Research integrity | 0.003 | 0.009 |
| 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".