AB020. The role of telehealth interventions on clinical outcomes in patients with chronic hepatitis C infection: a systematic review
Bibliographic record
Abstract
Background: Despite the development of effective treatments and vaccines, chronic viral hepatitis remains the predominant cause of liver cirrhosis and hepatocellular carcinoma. Only 1% have adequate access to treatment globally. Telemedicine has gained wider attention during coronavirus disease (COVID-19) and has been proposed as an alternative to traditional healthcare modalities. This review examined and synthesized available data on the impact of telemedicine on health outcome and patterns of usage of healthcare services in chronic HCV patients, aiming to provide evidence for potential telehealth interventions in improving accessibility to HCV treatment. Methods: Databases including PubMed and EMBASE were searched to compare the impact of telemedicine vs. standard care on chronic HCV patients’ rates of sustained virologic response, treatment completion rates and loss to follow-up. The qualities of included studies were assessed by a mixed methods appraisal tool. Relative risk (RR) estimates were pooled by meta-analyses and stratum-specific analyses were performed to evaluate differences by nationality and telemedicine modalities. Results: Twelve observational studies conducted in Canada, the United States, Australia, Taiwan and Mexico were included. Sustained virologic response rates attained in telemedicine were equivalent to that attained in traditional healthcare modalities. The pooled RR of sustained virologic response rates in participants treated by telemedicine was 0.864 [95% confidence interval (CI): 0.769–0.960]. Compared to standard modalities, chronic HCV patients receiving telemedicine had a higher compliance rate [pooled RR of treatment completion rate: 0.541 (95% CI: 0.256–0.826)], lower dropout rate [pooled RR of loss to follow-up: 0.555 (95% CI: 0.132–0.978)]. Major barriers in standard modalities included geographic inconvenience, financial constraints, and psycho-social challenges. Conclusions: Telemedicine for HCV management yields equivalent clinical outcomes to standard care, while circumventing the limitations of geographic and economic barriers. Its roles in facilitating in-between visit monitoring and promoting health empowerment have been demonstrated. Further research is yet required to determine optimal ways for telemedicine integration into routine clinical care management.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".