Ethical, legal, and practical concerns about recontacting patients to inform them of new information: The case in medical genetics
Bibliographic record
Abstract
There is a consensus among medical geneticists that it is desirable to recontact patients as new information becomes available. Furthermore, some have suggested that there are legal arguments to support an obligation, creating a duty to recontact. Thus far much of the discussion among medical geneticists has focused on the practical concerns of implementing such a policy. However, we think that any such policy raises a number of important ethical concerns that must first be considered. Furthermore, there has not been a careful evaluation of the legal precedents that may reflect on a hypothetical duty to recontact. In this paper we first present an analysis of the scope of approaches and issues to be addressed in the development of ethical policy on this question. Secondly, we examine whether there is a legal obligation to recontact former patients about advances in genetics, as well as the legal implications if such a policy were to be adopted. Finally, we consider some of the functional and resource implications of adopting a policy of recontact. Our goal is to provide a framework for further discussion of this question and to stimulate further debate and research. © 2001 Wiley-Liss, Inc.
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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 | 0.059 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.063 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.042 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".