Identifying opportunities for hepatic encephalopathy self-management: A mixed methods systematic review and synthesis
Bibliographic record
Abstract
Background: Hepatic encephalopathy (HE) in cirrhosis is an extremely challenging complication for patients and care partners. To identify potentially modifiable factors to enhance HE self-management strategies, we conducted a synthesis of quantitative and qualitative research about real-world HE behaviours, knowledge, and experiences. Methods: Using the EPPI-Centre's mixed methods synthesis procedure, a systematic literature search in five databases was completed; methods of selected articles underwent critical appraisal followed by descriptive analysis and coded line-by-line of content. Through refutational translation, the findings from the quantitative and qualitative syntheses were juxtaposed to highlight congruencies, incongruencies, or gaps. These findings informed generation of cross-analytical themes that were transformed into action statements. Results: = 17) generated four themes (patients had low awareness of HE and low treatment adherence rates, physicians had a non-uniform approach to non-pharmaceutical therapies). Meta-aggregation of qualitative data from six articles yielded three themes (patients and care partners had low levels of HE awareness, were unfamiliar with HE self-management, and were adherent to treatments). Comparison of findings revealed three congruencies, two gaps, and one incongruency. The combined synthesis yielded two self-management themes: universal patient-oriented cirrhosis HE education and ensuring each health care encounter systematically addresses HE to guarantee health care is continuously modified to meet their needs. Conclusions: By drawing on elements of Bloom's Taxonomy and distributed knowledge networks, deliberate patient-oriented HE messaging at all health care encounters is greatly needed to improve health outcomes and reduce care burdens related to HE.
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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 | 0.113 | 0.198 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".