Informed Consent in the Twenty‐First Century: What It Is, What It Isn't, and Future Challenges in Informed Consent and Shared Decision Making
Bibliographic record
Abstract
Abstract Consent evolved from judge‐made law in Great Britain in 1767. The term informed consent entered the judicial lexicon in 1957. The first court case to articulate a reasonable person standard adopted by the high courts in Canada and Australia was heard in the U.S. in 1972. Today, informed consent continues to develop in four areas: (i) the court‐based doctrines of consent and informed consent in clinical care in judge‐made law; (ii) federal regulations related to research on human study participants; (iii) shared decision making adopted by care organizations and medical societies in the US, Canada, and Europe; and (iv) areas including decision analysis, discourse analysis, ethics, linguistic analysis, patient–physician communication, risk and evidence communication, and social theory. In this paper, we will focus on consent and informed consent in the first part of the twenty‐first century. We will examine a range of information and decision making frameworks from the oldest court‐established frameworks of consent and informed consent to recent conceptions of information and decision making in evidence‐based decision making and shared decision making in the patient–physician relationship. This paper is divided into three parts: I. What informed consent is, II. What informed consent isn't, and III. Future challenges in informed consent and shared decision making.
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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 | 0.157 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.158 |
| Scholarly communication | 0.028 | 0.039 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.021 | 0.024 |
| 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".