Abstract P308: Can a Customized Quantitative Informed Consent Document Improve Decision Quality and Be Integrated Into the Routine Process of Care
Bibliographic record
Abstract
BACKGROUND: An informed consent requires reference to the risks of treatment. At best, most informed consent documents list the risks but make no specific reference to their probability. We developed a computer-based system that generates a customized quantitative informed consent document for patients considering CABG or PCI. METHODS: We used our northern New England PCI and CABG registries and published data to develop clinical prediction rules for patient specific estimates of the morbidity and mortality following diagnostic cardiac catheterization, ad hoc PCI, staged PCI, and CABG. Preoperative patient and disease characteristics include: age, gender, comorbidities, prior intervention, coronary anatomy (if available) and urgency. A computer-based interface was developed to easily acquire patient information and print informed consent documents with patient-specific risks. Focus groups were conducted with revascularized patients who were asked to describe their informed consent experience and their preferences for the presentation of risk. RESULTS: Focus group participants indicated they prefer display of numbers and not percentages. Below is an example: “I understand that the following risks, among others, are associated with the procedure(s): If 100 people like me have a CABG procedure about... 99 may survive and 1 may die in the hospital 99 may avoid a stroke and 1 person may have a stroke in the hospital 98 may avoid a serious complication (including infection of your chest wall, heart attack, kidney failure, and others) and 2 may have one of these problems in the hospital 91 may avoid a lesser complication (including kidney injury, bleeding, and others) and 9 may have these problems in the hospital” Physicians at one medical center tested the computer-based prototype. All found the data needed was readily available and the prototype easy to fill out and print. In, general patients liked the presentation and felt that it helped to stimulate some additional conversations. CONCLUSION: We designed a computerized system to electronically calculate risk. Clinical data needed to complete the risk assessment was readily available. Creating a customized quantitative informed consent document in “real time” for patients having CABG or PCI is possible.
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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 | 0.082 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.146 | 0.059 |
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".