Telemedicine for Follow-up Management of Patients After Liver Transplantation: Cohort Study
Bibliographic record
Abstract
BACKGROUND: Technical capabilities for performing liver transplantation have developed rapidly; however, the lack of available livers has prompted the utilization of edge donor grafts, including those donated after circulatory death, older donors, and hepatic steatosis, thereby rendering it difficult to define optimal clinical outcomes. OBJECTIVE: We aimed to investigate the efficacy of telemedicine for follow-up management after liver transplantation. METHODS: To determine the efficacy of telemedicine for follow-up after liver transplantation, we performed a clinical observation cohort study to evaluate the rate of recovery, readmission rate within 30 days after discharge, mortality, and morbidity. Patients (n=110) who underwent liver transplantation (with livers from organ donation after citizen's death) were randomly assigned to receive either telemedicine-based follow-up management for 2 weeks in addition to the usual care or usual care follow-up only. Patients in the telemedicine group were given a robot free-of-charge for 2 weeks of follow-up. Using the robot, patients interacted daily, for approximately 20 minutes, with transplant specialists who assessed respiratory rate, electrocardiogram, blood pressure, oxygen saturation, and blood glucose level; asked patients about immunosuppressant medication use, diet, sleep, gastrointestinal function, exercise, and T-tube drainage; and recommended rehabilitation exercises. RESULTS: No differences were detected between patients in the telemedicine group (n=52) and those in the usual care group (n=50) regarding age (P=.17), the model for end-stage liver disease score (MELD, P=.14), operation time (P=.51), blood loss (P=.07), and transfusion volume (P=.13). The length and expenses of the initial hospitalization (P=.03 and P=.049) were lower in the telemedicine group than they were in the usual care follow-up group. The number of patients with MELD score ≥30 before liver transplantation was greater in the usual care follow-up group than that in the telemedicine group. Furthermore, the readmission rate within 30 days after discharge was markedly lower in the telemedicine group than in the usual care follow-up group (P=.02). The postoperative survival rates at 12 months in the telemedicine group and the usual care follow-up group were 94.2% and 90.0% (P=.65), respectively. Warning signs of complications were detected early and treated in time in the telemedicine group. Furthermore, no significant difference was detected in the long-term visit cumulative survival rate between the two groups (P=.50). CONCLUSIONS: Rapid recovery and markedly lower readmission rates within 30 days after discharge were evident for telemedicine follow-up management of patients post-liver transplantation, which might be due to high-efficiency in perioperative and follow-up management. Moreover, telemedicine follow-up management promotes the self-management and medication adherence, which improves patients' health-related quality of life and facilitates achieving optimal clinical outcomes in post-liver transplantation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".