Telehealth interventions in patients with chronic liver diseases: A systematic review
Bibliographic record
Abstract
BACKGROUND AND AIM: Telehealth interventions may improve access to care, disease-specific, and quality outcomes in chronic liver diseases (CLDs). We aimed to systematically evaluate outcomes of telehealth interventions in CLDs. MATERIALS AND METHODS: We used key terms and searched PubMed/EMBASE from inception to January 10, 2022. Two authors independently screened abstracts. Disagreements were resolved by a third reviewer. We included any type of CLD, including posttransplant patients, and extracted outcomes as defined by authors for each etiology of CLD (sustained virological response in HCV or weight loss in NAFLD). Meta-analysis was not performed because of the heterogeneity of data. Quality assessment was performed using the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias tool for clinical trials. RESULTS: Of 4250 studies screened, 43 met the inclusion criteria. Of these, 28 reported HCV treatment outcomes. All studies showed no statistically significant differences between sustained virological response rates in TH groups compared with control groups or historic cohorts. Eight studies evaluating liver transplant-related processes and outcomes demonstrated improved rates of transplant evaluation and referrals and decreased short-term readmission rates. Three randomized controlled trials and 1 observational study on NAFLD showed improved weight loss outcomes. One retrospective study showed reduced mortality risk in CLD patients with at least 1 TH encounter. CONCLUSIONS: TH interventions in patients with CLDs consistently show equivalent or improved clinical outcomes compared with traditional encounters. TH in CLDs can bridge the gap in access while maintaining the quality of care for underserved populations.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".