Advance care planning: the journey of changing hospital culture in an in-centre dialysis unit
Bibliographic record
Abstract
The mortality rate for patients with end stage renal disease is comparable to or higher than many cancer diagnoses. While dialysis provides life-sustaining treatment, kidney failure is irreversible and is often accompanied by many complications such as diabetes, cardiovascular disease and peripheral neuropathy, among others. Essentially, the treatment of kidney failure by renal replacement therapy is a long-term palliative therapy. In light of this reality, the Providence Health Care Renal program launched the Renal End of Life Initiative (RELI) in 2007 to ensure a continuum of quality kidney care from diagnosis through the terminal phase of life, including bereavement care for the patient's survivors. The RELI focuses on the individual needs of the patient and family in four main components: pain & symptom management, Advance Care Planning (ACP), palliative care training for inpatient nurses, and bereavement support. The goal of the RELI was to demonstrate that with a small amount of seed funding and the aligned commitment of physicians, staff and administrative leadership, a compassionate and integrated program of palliative and bereavement care for patients and families living with chronic disease is achievable. Focusing on ACP, specific communication tools were developed to track the progress of ACP conversations with patients and were integrated into patients' medical charts. Within less than a year almost 50% of patient records had ACP conversations documented. Viewers of this presentation will learn how a systematic approach to ACP can be used innovatively to enhance care for patients living with a chronic progressive illness.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".