Bibliographic record
Abstract
Genetic technology has enabled us to test fetuses for an increasing number of diseases and impairments. On the basis of this genetic information, prospective parents can predict – and prevent – the birth of children likely to have those conditions. In developed countries, prenatal genetic testing has now become a routine part of medical care during pregnancy. Underlying and driving the spread of this testing are controversial assumptions about health, impairment, and quality of life. While the early development of prenatal testing and selective abortion may have been informed by the questionable view that they were just another form of disease and disability prevention, these practices are now justified largely in other terms: prospective parents should be permitted to make reproductive decisions based on concern for the expected quality of their children's lives. These practices, and their prevailing rationale, reinforce a trend in biomedical ethics that began in the 1970s, one giving a central role to quality of life in health care decision making. In this Introduction, we will briefly review how quality of life came to assume such importance in health care and reproductive practice and policy. We will then discuss some of the conceptual and ethical issues raised by attempts to measure health-related quality of life and to use such measures in the evaluation of health care interventions. Next, we will examine the bearing of these issues on the current rethinking of disability, a category that has been widely associated with poor quality of life.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.346 | 0.185 |
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".