Nonreimbursed Costs Incurred by Living Kidney Donors: A Case Study From Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Living donors may incur out-of-pocket costs during the donation process. While many jurisdictions have programs to reimburse living kidney donors for expenses, few programs have been evaluated. METHODS: The Program for Reimbursing Expenses of Living Organ Donors was launched in the province of Ontario, Canada in 2008 and reimburses travel, parking, accommodation, meals, and loss of income; each category has a limit and the maximum total reimbursement is $5500 CAD. We conducted a case study to compare donors' incurred costs (out-of-pocket and lost income) with amounts reimbursed by Program for Reimbursing Expenses of Living Organ Donors. Donors with complete or partial cost data from a large prospective cohort study were linked to Ontario's reimbursement program to determine the gap between incurred and reimbursed costs (n = 159). RESULTS: The mean gap between costs incurred and costs reimbursed to the donors was $1313 CAD for out-of-pocket costs and $1802 CAD for lost income, representing a mean reimbursement gap of $3115 CAD. Nondirected donors had the highest mean loss for out-of-pocket costs ($2691 CAD) and kidney paired donors had the highest mean loss for lost income ($4084 CAD). There were no significant differences in the mean gap across exploratory subgroups. CONCLUSIONS: Reimbursement programs minimize some of the financial loss for living kidney donors. Opportunities remain to remove the financial burden of living kidney donors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".