Telemedicine approaches for patients with cirrhosis, including vulnerable populations: A narrative review
Bibliographic record
Abstract
Background: The COVID-19 pandemic accelerated the adoption of telemedicine in health care. However, telemedicine in cirrhosis care remains underexplored. In particular, patients with alcohol use disorder (AUD) and hepatitis C virus (HCV) may be overrepresented among vulnerable populations, but have limited access to telemedicine. Method: We performed a literature review on telemedicine approaches for patients with cirrhosis as well as patients with AUD and HCV with or without cirrhosis. Peer-reviewed studies involving direct patient-physician interactions were searched on PubMed and Google Scholar. Keywords used included cirrhosis, AUD, HCV, and telemedicine. Abstracts were screened. Full texts were reviewed. Results: Among patients with cirrhosis, videoconferencing at satellite sites shortened the time from liver transplant referral to evaluation and listing. Telephone calls were less effective, especially for those with decompensated cirrhosis. Among patients with AUD, videoconferencing at satellite sites was effective, with patients being five times more likely to be prescribed medications. Treatment programs involving videoconferencing and telephone calls demonstrated retention rates above 50%. Among patients with HCV, videoconferencing was effective, with high (>90%) sustained virological response rates. Across all approaches, concerns raised included audiovisual quality, patient privacy, and licensing restrictions. Conclusion: Videoconferencing at satellite sites is most promising if audiovisual quality and other barriers are optimized. Telemedicine may not be appropriate for management of decompensated cirrhosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".