Ethical and Legal Implications of Frailty Screening
Bibliographic record
Abstract
Goals of screening for frailty include (a) promoting healthy aging, (b) addressing frailty with preventive and targeted interventions, (c) better aligning social and medical responses to frailty with the needs of frail older adults and (d) preventing harms to frail older adults from excessive and inappropriate medical interventions that are insensitive to the implications of frailty. However, the medicalization of frailty and outcomes of the screening process also risk harming frail older adults and their autonomy through stereotyping and by legitimizing denial of care. This risk of harm gives rise to ethical and legal questions and considerations that this paper addresses. Frailty screening that is ethically defensible will situate and support healthcare that is consistent with people's needs, circumstances and capacity to benefit from the care provided. We also call for an informed consent process that incorporates supported or shared decision making in order to protect the autonomy of frail older adults.
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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 | 0.133 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.050 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.028 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".