The development of an online palliative care toolkit for cirrhosis
Bibliographic record
Abstract
Background: Liver cirrhosis carries high symptom burden and health care utilization. Palliative care (PC) has demonstrated improvements in symptom burden and overall quality of life. Despite this, PC remains underutilized in patients with chronic liver disease. As a step to bridge this gap, we developed cirrhosiscare.ca as an easy-to-access, evidence-, and expert consensus-based online PC resource for health care providers. Methods: This short report describes the development of the health care practitioner 'Symptom Management' section of www.cirrhosiscare.ca, covering common symptoms (e.g., pain, pruritus, anxiety, depression, nausea/vomiting) experienced by patients with cirrhosis. A multidisciplinary team included representatives from hepatology, palliative care, pain management, psychiatry, pharmacy, and primary care. The project was carried out over four phases: Symptom selection, Material development, Website creation, and Feedback, revisions, and maintenance. Results: The cirrhosiscare.ca website was launched to the public on March 15, 2021. Twelve detailed content pages were developed alongside four end-of-life algorithms. The developed pages have been well received, and there are between 10,000 and 12,000 new users on the site each month. Conclusions: The development of the cirrhosiscare.ca website is a digital strategy to increase awareness and access to evidence-based and expert consensus-based materials for PC. Ongoing dissemination and sustainability will include regular maintenance and updates to online content, links to inpatient care pathways, and incorporation within the Alberta one:carepath primary care initiative.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".