Outcomes of telehealth care for lung transplant recipients
Bibliographic record
Abstract
Telehealth uses videoconferencing to provide long-distance clinical care. Experience with telehealth in the setting of organ transplantation is limited. The purpose of this cohort study was to compare the impact of telehealth vs in-person follow-up of lung transplant recipients. Telehealth eligible patients were three or more years post-transplant and resided in Ontario outside the Greater Toronto Area. Patients with initial telehealth visits between July 1, 2009, and Dec 31, 2014, were retrospectively reviewed to assess outcomes of chronic lung allograft dysfunction progression and mortality until December 31, 2016, compared with eligible patients seen in-person. Of eligible patients (n = 204), 119 (58.3%) were seen via telehealth. Most patients (97%) rated telehealth as equivalent or superior to clinic visits. Telehealth visits resulted in significant out-of-pocket cost savings and travel distance savings for patients. There was no significant difference in mortality from the time of first visit (HR 0.81, 95% CI 0.49-1.32, P = 0.4) or from the time of transplant between groups (HR 0.72, 95% CI 0.43-1.17, P = 0.2). Telehealth can safely and effectively be used in select transplant recipients to increase access to care and reduce time and financial burdens for patients residing greater distances from primary transplant centers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".