A Healthcare Cost Calculator for Older Patients Over the First Year After Renal Transplantation
Bibliographic record
Abstract
Forecasting tools that accurately predict post-transplantation healthcare use of older end-stage renal disease (ESRD) patients are needed at the time of transplantation in order to ensure smooth care delivery in the post-transplant period. We addressed this need by developing a machine-learning-based calculator that predicts the cost of healthcare for older recipients of a deceased-donor kidney over the first year following transplantation. Regression tree and regularized linear regression methods, including ridge regression, lasso regression and elastic net regression were explored on all cases of deceased-donor renal transplants performed for patients aged over 60 in Ontario, Canada between March 31, 2002 and April 31, 2013 (N=1328), The optimal model (lasso) identified age, membership of one of 14 regionalized Local Health Integration Networks, blood type, sensitization, having diabetes as the primary case of ESRD, total healthcare costs in the 12-month pre-workup period and the 6-month workup period to be inputs to the cost calculator. This cost calculator, in conjunction with clinical outcome information, will aid health system planning and performance to ensure better management of recipients of scarce kidneys.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".