The SmartNTx-study: a prospective, randomized controlled trial to investigate additional interventional telemedical management versus standard aftercare in kidney transplant recipients
Bibliographic record
Abstract
Background Regular follow-up care after kidney transplantation is performed in transplant centers together with local nephrologist practices in Germany. Patients after kidney transplantation have to fulfill many tasks and manage their disease, follow a complex therapeutic regimen, communicate with the transplant center and home nephrologists, and coordinate doctor appointments. It has been shown that mHealth solutions such as mobile phone applications (apps) can support patients in their self-management. However, stand-alone apps have limitations and ideally, the mHealth solutions are embedded in a holistic treatment approach, including healthcare professionals. Methods We will conduct a 1-year, prospective, randomized, 2-armed, parallel group multicenter trial in three German Kidney Transplant Centers (KTCs) to demonstrate that additional and continuous interventional telemedical management will improve health after kidney transplantation in patients of all ages. Therefore, a composite endpoint of seven key outcome variables [fewer hospitalizations, shorter length of hospitalization, less development of de novo donor-specific antibody (DSA), better medication adherence, lower tacrolimus intra-patient variability, better blood pressure control, and better renal function after kidney transplantation]was defined. All the patients will receive the same routine post-transplant aftercare. The patients in the interventional arm will receive additional predefined telemedical management, including regular telemedicine visits and automatic bidirectional data transfer (e.g., vital signs, wellbeing, medication plan, and laboratory data together with a chat option) between the patient at home and the KTC through a certified smartphone app. If necessary, a home nephrologist can be included in the automatic data transfer. In the interventional arm, the iBox score will be used to better detect patients at risk for early graft failure and drug-drug interactions will be regularly checked with certified software. Discussion The study aims to prolong patient and graft survival through additional telemedical services in order to reduce avoidable hospitalizations, improve treatment of co-morbidities, and improve adherence through patient empowerment, which should result in lower health care costs, and better quality of life of patients after kidney transplantation. Clinical Trial Registration ClinicalTrials.gov , identifier NCT05897047.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | medium |
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".