Bibliographic record
Abstract
This is the last in a series of five articles Measuring quality of life has an important place in health care, but what about when life has no quality? Or worse? From an ethical perspective there are two areas in which these issues have been extensively explored: termination of pregnancy and end of life decision making for competent and non-competent adults. One way in which quality of life is sometimes introduced to decision making is through the concept of “a life not worth living.” The seemingly logical conclusion is that lives not worth living may not be worth creating or saving. This final paper in the series debates the problems—both the practical difficulties of measurement and ethical issues—associated with measuring quality of life in situations in which lives have been judged to have no quality. #### Summary points There are no quality of life measures that reliably identify patients who feel that life is not worth living Basing management decisions on such measures requires extreme caution because of the fluctuating nature of patients' valuations of life and their desire for death Patients who are dying may find some quality in life, even when their quality of life as assessed by current measures is abysmal The use of proxies to determine whether a life is worth living is problematic because of the possible disparity between an observer's assessment and the patient's own valuation Both patients and their proxies have identified health states that they consider to be worse than death If a pregnancy is terminated because the fetus has an abnormality we will never know for sure whether the life in question would have been worth living. However, we allow lives to be terminated if they are predicted to be of low (or maybe only slightly diminished) quality. We base these decisions not only, …
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".