Evaluation of Virtual Care in Kidney Transplant Recipients in the Early Posttransplant Period
Bibliographic record
Abstract
ABSTRACT Background Though virtual care was widely adopted during the COVID‐19 pandemic, evidence to support its use in kidney transplant recipients early after transplantation is limited. Methods We conducted a retrospective cohort study comparing post kidney transplant outcomes in patients who received in‐person transplant care before the COVID‐19 pandemic with those who received mainly virtual transplant care during the COVID‐19 pandemic. The usual‐care group included 69 patients who received a kidney transplant from March 1, 2019 to September 1, 2019, and the virtual‐care group included 64 patients who received a kidney transplant from September 1, 2020 to March 1, 2021. Results At 6 months, five patients in the usual‐care group and three patients in the virtual‐care group died. There was one graft loss and one episode of acute rejection in the usual‐care group, and two episodes of acute rejection in the virtual‐care group (p = 0.60). Estimated glomerular filtration rate was higher for patients in the virtual‐care group (59 mL/min/1.73 m2 vs. 52 mL/min/1.73 m2, p = 0.046) and serum creatinine was not different (138 µmol/L vs. 127 µmol/L, p = 0.27). There was no difference in mean blood pressure or hospitalizations. Conclusion Outcomes were similar among recipients of a kidney transplant prior to the COVID‐19 pandemic when care was mainly in person and during the pandemic when care was mainly virtual, without a signal of harm. Patient and donor selection may have led to unmeasured differences between groups.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".