Insurability of Living Organ Donors: A Systematic Review
Bibliographic record
Abstract
Being an organ donor may affect one's ability to obtain life, disability and health insurance. We conducted a systematic review to determine if insurability is affected by living organ donation, and if concern about insurability affects donor decision making. We searched MEDLINE, EMBASE, SCI, EconLit and Cochrane databases for articles in any language, and reviewed reference lists from 1966 until June 2006. All studies discussing the insurability of living organ donors or its impact on donor decision making were included. Data were independently abstracted by two authors, and the methodological quality appraised. Twenty-three studies, from 1972 to 2006, provided data on 2067 living organ donors, 385 potential donors and 239 responses from insurance companies. Almost all companies would provide life and health insurance to living organ donors, usually with no higher premiums. However, concern about insurability was still expressed by 2%-14% of living organ donors in follow-up studies, and 3%-11% of donors actually encountered difficulties with their insurance. In one study, donors whose insurance premiums increased were less likely to reaffirm their decision to donate. Based on available evidence, some living organ donors had difficulties with insurance despite companies reporting otherwise. If better understood, this potential barrier to donation could be corrected through fair health and underwriting policies.
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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 | 0.009 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".