Infection, Rejection, and Hospitalizations in Transplant Recipients Using Telehealth
Bibliographic record
Abstract
Context Telehealth technology serves individuals who live in geographical areas that prohibit easy access to specialized health care and can provide transplant recipients with access to transplant center personnel for adjunctive follow-up care. Objective To compare infection, rejection, and hospitalization events in subjects randomized to telehealth or to standard posttransplant care. Study Design, Study Participants, Setting and Research Procedure This longitudinal prospective study compared transplant outcomes (infections, rejections, and hospitalizations) of 106 subjects who were randomized to either the telehealth (n=53) or standard care (n=53) group and met the 6-month study end point. Sex, race, and transplant type were evenly distributed within the 2 groups. Subjects received primary follow-up care from nurse practitioners. The telehealth visits were conducted via live interactive sessions with digitized equipment used to perform physical examinations. Main Outcomes Infections, rejections, and hospitalizations were summarized for each of the groups. Subgroup analyses were performed by sex, transplant type, and time since transplant. Results No differences were found between the telehealth and standard care groups for infections, rejections, or hospitalizations at the 6-month data end point. Overall, females had twice as many infections as males ( P = .01). In this analysis, group assignment did not affect study outcomes. Conclusions The rates of infection, rejection, and hospitalization in a sample of primarily long-term transplant patients did not differ between patients who received telehealth follow-up and patients who received standard care, indicating that this delivery system can be used to provide follow-up care after transplant.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".