Projecting the Number of Posttransplant Clinic Visits With a Rise in the Number of Kidney Transplants: A Case Study From Ontario, Canada
Bibliographic record
Abstract
Background:In Ontario, kidney transplants have risen by 4% annually in recent years. An understanding of how this will affect the future annual number of posttransplant follow-up visits informs how to organize and deliver care.Objective:We projected the required number of annual posttransplant follow-up nephrology visits to inform posttransplant care delivery.Design:Population-based retrospective cohort study.Setting:Linked databases from Ontario, Canada (population 14 million).Patients:Incident kidney transplant recipients from years 2008 to 2013.Measurements:Frequency, distance traveled, and current and projected visits for posttransplant follow-up.Methods:Assuming a graft survival of 13 years and using the mean number of posttransplant clinic visits in years 1, 2, and 3, we forecasted the number of clinic visits needed in the year 2027.Results:Using data from 2443 recipients, the mean (SD) number of clinic visits per recipient was 14.0 (9.2) in the first year after transplant, and 3.9 (6.2) and 3.0 (5.3) in the second and third year, respectively. If transplant rates rise by 4% per year until 2027, the estimated annual visits number will increase from 30 622 to 43 948. The median (25th, 75th percentile) distance between transplant center and patient’s home was 30 (13, 65) km. The median round-trip travel distance for these visits in the first year after transplantation was 603 km per recipient, and median driving cost was Can$344 (2017).Limitations:Regarding patient expense, limitations include that distances traveled were calculated orthodromically, and we did not account for patient cost of follow-up beyond that of vehicular travel. Regarding follow-up projections, limitations include the assumption that graft life span will not change, follow-up patterns do not differ between donor kidney type, and we did not survey stakeholders as to their preferred method of follow-up.Conclusion:We quantified the increase in posttransplant visits when regional annual rates of transplantation rise. Strategies recognizing the burden of these visits may enhance patient-centered care, as it is unclear how some patients manage costs, nor how the current health care system will manage the demand.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".