P37 Nutritional Education Resource Developed for Patients Living With Cirrhosis: Impact of Hepatic Encephalopathy
Bibliographic record
Abstract
Introduction: Liver disease affects 1 in 4 Canadians. One of the most common complications of chronic liver disease is malnutrition, which is associated with poor quality of life and prognosis. Nutritional education resources for this population are lacking. Purpose: The general objective is to assess the impact of the evidence-based Nutrition in Cirrhosis Guide on patients living with cirrhosis. Specifically, the aim is to assess malnutrition risk, nutritional knowledge and quality of life as well as the impact of history of hepatic encephalopathy (HE) on nutritional knowledge over 6 months. Methods: An on-going randomized controlled study including 100 patients with cirrhosis divided in 2 groups: Guide+ (n=50) and Guide- (n=50). All patients are assessed for malnutrition risk (Liver Disease Undernutrition Screening Tool), knowledge (questionnaire based on the Guide) and quality of life (Chronic Liver Disease Questionnaire) at baseline, 3 and 6 months. History of HE is collected. The Guide+ group of patients are provided with the Guide for 6 months and Guide- patients are not. Preliminary Results: To date, 23 patients have completed the study: Guide+ (n=11) and Guide- (n=12). Characteristics of participants are comparable at baseline. The preliminary results show a significant improvement of nutritional knowledge for Guide+ patients (from 76.2% at baseline to 84,4% after 6 months, P=0.016). The Guide- patients’ knowledge remained unchanged throughout the study. In the Guide+ group, 2 patients had a history of HE which did not lead to improvement of nutritional knowledge (from 82,0% at baseline to 77,0% after 6 months), whereas in patients without history of HE, improvement was observed (74,7% at baseline to 86,0% after 6 months). No significant changes were observed in quality of life and malnutrition risk. Conclusion: There is a significant improvement of patients’ nutritional knowledge following 6 months of using the Guide. However, preliminary results suggest that a history of HE could possibly impact their level of nutritional knowledge overtime. This denotes HE may play a role in learning new knowledge and patients with HE may require closer follow-up in regards to nutritional guidance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".