Serious genetic disorders: Can or should they be defined?*
Bibliographic record
Abstract
The word "serious" appears in laws and policies regarding genetics services but is not defined. Genetics professionals would most likely be consulted if definitions are made. We surveyed all U.S. board-certified genetics services providers and all members of the European Society of Human Genetics (ESHG), Canadian College of Medical Geneticists (CCMG), and Ibero-American Society of Human Genetics (IASHG), using anonymous, mailed questionnaires. Respondents were asked to list three conditions they considered lethal, three that were serious but not lethal, and three that were not serious. Of 3,317 asked to respond, 1,481 (45%) returned questionnaires. Analysis was limited to responses of the 1,264 (85%) who saw patients. Respondents listed 537 conditions, with extensive overlap between categories; 46% of conditions listed as serious were also listed as not serious and 41% were listed as lethal. Respondents did not want professional societies, laws, or national ethics committees to define serious. They favored individual patients as decision makers, with the help of individual doctors. Their reasons were that genetic disorders vary in expression, individuals perceive disorders differently, professional or legal definitions could be unfair to minority groups, definitions tend to be inflexible when new treatments appear. In the United States, most would not use a definition of serious to limit reproductive services, carrier screening, or neonatal intensive care; outside the United States, more supported limitations. There is not sufficient consensus among experienced genetics professionals to define serious genetic conditions for purposes of law or policy. Responses point to social and economic influences on perceptions of what constitutes a serious genetic disorder.
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 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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".