Informed Consent and Informed Decision-Making in High-Risk Surgery: A Quantitative Analysis
Bibliographic record
Abstract
BACKGROUND: Informed consent is an ethical and legal requirement that differs from informed decision-making-a collaborative process that fosters participation and provides information to help patients reach treatment decisions. The objective of this study was to measure informed consent and informed decision-making before major surgery. STUDY DESIGN: We audio-recorded 90 preoperative patient-surgeon conversations before major cardiothoracic, vascular, oncologic, and neurosurgical procedures at 3 centers in the US and Canada. Transcripts were scored for 11 elements of informed consent based on the American College of Surgeons' definition and 9 elements of informed decision-making using Braddock's validated scale. Uni- and bivariate analyses tested associations between decision outcomes as well as patient, consultation, and surgeon characteristics. RESULTS: Overall, surgeons discussed more elements of informed consent than informed decision-making. They most frequently described the nature of the illness, the operation, and potential complications, but were less likely to assess patient understanding. When a final treatment decision was deferred, surgeons were more likely to discuss elements of informed decision-making focusing on uncertainty (50% vs 15%, p = 0.006) and treatment alternatives (63% vs 27%, p = 0.02). Conversely, when surgery was scheduled, surgeons completed more elements of informed consent. These results were not associated with the presence of family, history of previous surgery, location, or surgeon specialty. CONCLUSIONS: Surgeons routinely discuss components of informed consent with patients before high-risk surgery. However, surgeons often fail to review elements unique to informed decision-making, such as the patients' role in the decision, their daily life, uncertainty, understanding, or patient preference.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".