Estimated Impact of Deemed Consent Legislation for Organ Donation on Individuals With Kidney Failure: A Dynamic Decision Analytic Model
Bibliographic record
Abstract
Background:There is little data modeling the impact of deemed consent legislation (eligible individuals who do not register their decision to decline to be a donor are presumed to consent after death) on outcomes for individuals with kidney failure.Objective:To estimate the change in life-years (LYs) and quality-adjusted life-years (QALYs) resulting from different changes in the rate of deceased donor kidney transplantation associated with deemed consent legislation and health system transformation.Design:Dynamic Decision Analytic Model.Setting:This modeling study included kidney failure patients in Atlantic Canada (all of whom receive their kidney transplants in Halifax, Nova Scotia). The adoption of deemed consent legislation was the intervention, and opt-in (the status quo) was the reference comparator.Patients:Prevalent kidney failure patients at the end of 2019 in all of Atlantic Canada (N = 3615) served as the starting population.Methods:We compared expected outcomes between the intervention and comparator. Changes in QALYs and total LYs were modeled under different changes to the proportion of patients receiving a deceased donor kidney transplant (from –10% to 20%) resulting from deemed consent relative to the status quo. Changes in QALYs and LYs were reported for 3 different time horizons (5, 10, and 30 years). Uncertainty around QALYs and total LYs was reported using 95% confidence intervals (CIs) constructed from a probabilistic sensitivity analysis using 1000 Monte Carlo Simulations.Results:The increase in QALYs ranged from 7 QALYs (95% CI: 5-10) with a 5% increase using a 5-year time frame to 882 QALYs (95% CI: 619-1144) with a 20% increase over a 30-year time frame. Parallel changes in total LYs were also observed. In contrast, decreases in deceased donor kidney transplantation resulted in a loss of QALYs (for example, –463 QALYs; 95% CI: –633 to –306 for a 10% decrease over a 30-year time frame). Using the most optimistic scenario (a 20% increase), there was an 18% increase in the cumulative number of deceased donor kidney transplant recipients over a 30-year observation period.Limitations:The results are subject to uncertainty depending on changes to the dialysis or transplant population that were not modeled and that may not be fully captured with probabilistic sensitivity analysis.Conclusions:Deemed consent legislation will lead to variable changes in QALYs and total LYs for the kidney failure population, depending on the degree to which deceased donor transplantation rates change and the time horizon of observation. This modeling study may serve as a baseline to monitor the future impact of deemed consent legislation.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".