Life Insurance for Living Kidney Donors: A Canadian Undercover Investigation
Bibliographic record
Abstract
Some living kidney donors encounter difficulties obtaining life insurance, despite previous surveys of insurance companies reporting otherwise. To better understand the effect of donation on insurability, we contacted offices of life insurance companies in five major cities in Canada to obtain $100 000 of life insurance (20-year term) for 40 fictitious living kidney donors and 40 paired controls. These profiles were matched on age, gender, family history of kidney disease and presence of hypertension. The companies were blinded to data collection. The study protocol was reviewed by the Office of Research Ethics. The main study outcomes were the annual premium quoted and total time spent on the phone with the insurance agent. All donor and control profiles received a quote, with no significant difference in the premium quoted (medians $190 vs. $209, p = 0.89). More time was spent on the phone for donor compared to control profiles, but the absolute difference was small (medians 9.5 vs. 7.0 min, p = 0.046). Age, gender, family history of kidney disease and new-onset hypertension had no further effect on donor insurability in regression analysis. We found no evidence that kidney donors were disadvantaged in the first step of applying for life insurance. The effect donation has on subsequent phases of insurance underwriting remains to be studied.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".