Healthcare Costs for the Evaluation, Surgery, and Follow-Up Care of Living Kidney Donors
Bibliographic record
Abstract
BACKGROUND: The healthcare costs to evaluate, perform surgery, and follow a living kidney donor for the year after donation are poorly described. METHODS: We obtained information on the healthcare costs of 1099 living kidney donors between April 1, 2004, and March 31, 2014, from Ontario, Canada, using comprehensive healthcare administrative databases. We estimated the cost of 3 periods of the living donation process: the predonation evaluation period (start of evaluation until the day before donation), perioperative period (day of donation until 30-days postdonation), and 1 year of follow-up period (after perioperative period until 1 year postdonation). We analyzed data for donors and healthy matched nondonor controls using regression-based methods to estimate the incremental cost of living donation. Costs are presented from the perspective of the Canadian healthcare payer (2017 CAD $). RESULTS: The incremental healthcare costs (compared with controls) for the evaluation, perioperative, and follow-up periods were CAD $3596 (95% confidence interval [CI], CAD $3350-$3842), CAD $11 694 (95% CI, CAD $11 415-CAD $11 973), and $1011 (95% CI, CAD $793-CAD $1230), respectively, totalling CAD $16 290 (95% CI, CAD $15 814-CAD $16 767). The evaluation cost was higher if the intended recipient started dialysis partway through the donor evaluation (CAD $886; 95% CI, CAD $19, CAD $1752). The perioperative cost varied across transplant centers (P < 0.0001). CONCLUSIONS: Although substantial costs of living donor care are related to the nephrectomy procedure, comprehensive assessment of costs must also include the evaluation and follow-up periods. These estimates are informative for planning future work to support and expand living donation and transplantation, and directing efforts to improve the cost efficiency of living donor care.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".