Telemedicine as an Option of Healthcare Services in monitoring Non Alcoholic Fatty Liver Disease (NAFLD) Patients Facing COVID-19 Pandemic
Bibliographic record
Abstract
Healthcare visits were reduced during COVID-19 pandemic causing the disturbance of the sustainability in NAFLD monitoring. Telemedicine acts to maintain connectivity between patients and healthcare professionals. This review aimed to assess telemedicine helps to monitoring in NAFLD. The database was searched from The PubMedCentral and ScienceDirect from 2020 to 2023. We assessed the Cochrane Risk of Bias (RoB) for randomized controlled trial (RCT) and the Newcastle-Ottawa scale for non-RCTs systematic reviews. Meta-analyses with random-effects model to determine the pooled mean difference (MD) and p-value. There were three RCT and two non-RCT were included (n=239) with 56.9% males and the mean age was 51.3 years. Median intervention lasted 5.5 months. The parameter were body weight (BW), body mass index (BMI), waist circumference, liver function (AST/ALT), lipid profile and HbA1c. Metaanalysis showed 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 were not significantly reduced. Other laboratory parameter were decreased from systematic reviews. Telemedicine through mobile-based applications could be an option for monitoring body weight and BMI in NAFLD patients facing the 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".