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Record W2921466886 · doi:10.1097/tp.0000000000002685

Nonreimbursed Costs Incurred by Living Kidney Donors: A Case Study From Ontario, Canada

2019· article· en· W2921466886 on OpenAlexafffundabout
Lianne Barnieh, Scott Klarenbach, Jennifer Arnold, Meaghan S. Cuerden, Greg Knoll, Charmaine E. Lok, Jessica M. Sontrop, Matthew S. Miller, G. V. Ramesh Prasad, Sebastian Przech, Amit X. Garg

Bibliographic record

VenueTransplantation · 2019
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of OttawaUniversity of AlbertaUniversity of TorontoMcMaster UniversityLondon Health Sciences CentreSt. Michael's HospitalWestern University
FundersCanadian Institutes of Health Research
KeywordsReimbursementMedicineTotal costCohortBusinessInternal medicineEconomicsAccountingHealth careEconomic growth

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.007
GPT teacher head0.230
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2019
Admission routes3
Has abstractyes

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