Direct and indirect costs incurred by Australian living kidney donors
Bibliographic record
Abstract
AIM: To describe the direct and indirect costs incurred by Australian living kidney donors. METHODS: A total of 55 living kidney donors from three centres in Perth, Australia and one centre in Melbourne, Australia (2010-2014) was studied. Forty-nine donors provided information on expenses incurred during the donor evaluation period and up to 3 months after donation. A micro-costing approach was used to measure and value the units of resources consumed. Expenses were grouped as direct costs (ground and air travel, accommodation, and prescription medications) and indirect costs (lost wages and lost productivity). Costs were standardized to the year 2016 in Australian dollars. RESULTS: The most common direct costs were for ground travel (100%), parking (76%), and post-donation pain medications or antibiotics (73%). The highest direct costs were for air travel (median $1986 [three donors]) and ground travel (median $459 [49 donors]). Donors also reported lost wages (median $9891 [37 donors]). The inability to perform household activities or care for dependants were reported by 32 (65%) and 23 (47%) donors. Total direct costs averaged $1682 per donor (median $806 among 49 donors). Total indirect costs averaged $7249 per donor (median $7273 among 49 donors). Total direct and indirect costs averaged $8932 per donor (median $7963 among 49 donors). CONCLUSION: Many Australian living kidney donors incur substantial costs during the donation process. Our findings inform the continued development of policies and programmes designed to minimize costs incurred by 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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".