Bibliographic record
Abstract
Abstract Advances in genomic technology have expanded the number of tests for which individuals can obtain additional risk estimates about their susceptibilities to disease. Genomic information that predicts disease risk is relevant to insurers because the amount that policyholders pay for insurance coverage is determined by assessing their level of disease risk. Without access to genomic information, insurers are concerned that individuals may purchase more insurance at unadjusted premiums, leading to adverse selection. However, not all genomic information is useful from an insurance viewpoint, and the complexity of interpreting genetic variants, coupled with the possibility of incidental findings, raises ethical issues with using applicants' genomic testing results. Moreover, some people are reluctant to undergo genetic testing or participate in genomic research because of the fear that they may have difficulty in obtaining insurance after disclosing their genomic results to insurers. As genomic testing becomes more prevalent, there are concerns that sections of the population will be denied insurance because of their genetic profile. The question of what governments should do about this is one that has been debated in many countries. Key Concepts Insurers request genetic test results from applicants to enable them to make an accurate assessment of their health risks in the underwriting process. However, patients and the public fear that they may have difficulty in obtaining insurance after disclosing their genomic results to insurers. Not all genetic tests are useful for insurance underwriting; this will depend on the clinical validity and utility of the test, the nature and penetrance of the disease and the availability of treatment or management strategies. Use of genomic data for insurance underwriting also raises ethical concerns due to the complexity of interpreting genomic variants and the possibility of discovering incidental findings from genomic data. The public's fear of genetic discrimination by insurers appears to have consequences for public health and genetic research programme initiatives. Moratoria or legislation to prohibit the use of genetic test results by insurers has been adopted by many countries to prevent insurance discrimination. However, other ways to regulate the use of genetic tests in insurance include human rights or privacy‐based approaches.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".