Evaluating Kidney Transplantation for Adults with End-stage Renal Disease
Bibliographic record
Abstract
Kidney transplantation is widely accepted as the optimal treatment for most adult patients with end-stage renal disease (ESRD). Due to the rising scarcity of donor kidneys and the ageing of the patient population, performance of kidney transplantation warrants careful clinical and economic considerations. This dissertation comprised three projects to examine kidney transplantation for treating adult ESRD patients in the contemporary era, including (1) the role of pre-existing psychosocial conditions on the survival of deceased-donor kidney transplant (DDKT) recipients; (2) the economic consequences of i) receiving a DDKT as an adult ESRD patient and ii) donating a kidney as an adult living donor; and (3) machine-learning-based algorithms for managing older adults who have received a DDKT at age beyond 60 years by predicting i) their risk of posttransplant mortality, ii) their status of being a high-cost user of publicly funded health care during the year of transplantation, and iii) their total health care expenditures over the first posttransplant year. Person-level linked administrative data of all cases of DDKT performed in Ontario, Canada between April 1, 2002 and March 31, 2013 were used in projects (1) and (3). The results of this dissertation confirmed psychosocial conditions being diagnosed at 1-year prior to entering dialysis to be a strong and independent risk factor of posttransplant mortality. The systematic review and meta-analysis suggested that while DDKT was generally cost-effective over dialysis, the benefits diminished for patients over the age of 60 years and might further decrease for those older patients with diabetes and/or cardiovascular disease and long wait times. For living kidney donors, they incurred costs between $900 to $19,900 (US dollar in 2019 values) from pre-donation evaluation to the first postoperative year. The machine learning studies implied the feasibility of incorporating data mining techniques to support the decision-making and care planning of older DDKT recipients. Jointly, these findings have implications for an array of clinical and resource allocation policies to optimize ESRD management and highlight the potential utility of novel statistical methods in transplant outcomes research.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".