Telemedicine as an Option for Monitoring Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) Patients Facing the COVID-19 Pandemic: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Healthcare visits were reduced during the COVID-19 pandemic, causing disturbances in sustainable MAFLD monitoring. Telemedicine acts to maintain connectivity between patients and healthcare professionals. This review aimed to assess the role of telemedicine in monitoring MAFLD during the pandemic. Databases searched included l PubMed Central and ScienceDirect from 2020 to 2023. Assessment with The Cochrane Risk of Bias for randomized controlled trials (RCTs) and the Newcastle-Ottawa scale for non-RCTs systematic reviews. Meta-analyses employing a random-effect model were performed to determine the pooled mean difference (MD) and p-value. The results showed three RCT and two non-RCT (n = 239) with 56.9% males and a mean age of 51.3 years. The median intervention duration was 5.5 months. The parameters assessed included body weight (BW), body mass index (BMI), waist circumference, liver function (AST/ALT), lipid profile, HbA1c, and others. Meta-analysis revealed that telemedicine had a significant effect on improving outcomes for BW (MD −2.81: 95% CI, −4.11, −1.51, p < 0.0001) and BMI (MD −1.01: 95% CI, −1.47, −0.55, p < 0.0001) compared to standard care, while the AST/ALT levels were not significantly reduced. Some biochemical markers decreased based on the systematic reviews. In conclusion, telemedicine using mobile-based applications could be an option for monitoring lifestyle modification in MAFLD patients facing the COVID-19 pandemic.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".