Patient Preference-based Treatment Thresholds and Recommendations
Bibliographic record
Abstract
BACKGROUND: Decision analysis (DA) and the probability-tradeoff technique (PTOT) are patient preference-based methods of determining optimal therapy for individuals. Using aspirin therapy for the primary prevention of stroke and myocardial infarction (MI) in elderly persons as an example, the objective of this study was to determine whether group-level treatment thresholds and individual-level treatment recommendations derived using PTOT are identical to those of DA incorporating the patients' own values. METHODS: Persons in a pilot study of the efficacy of aspirin in the prevention of stroke and MI were asked to participate. Participant values and utilities for pertinent health states (e.g., minor and major stroke, MI, major bleeding episode) were determined. Then, in three hypothetical clinical situations in which the chance of stroke or MI was varied, PTOT was used to directly determine treatment thresholds for aspirin therapy (i.e., the smallest reduction in MI or stroke risk for which participants would be willing to take aspirin). Using DA modeling, with the same probabilities of events as in the PTOT exercise and incorporating participants' own values, treatment thresholds for the three clinical situations were determined. The thresholds determined by the two approaches were compared. Finally, based on these treatment thresholds, using the best estimates of the efficacy of aspirin to prevent first-time stroke and MI, PTOT and DA treatment recommendations for individual participants were compared. RESULTS: The 42 participants reported that a major stroke was the least desirable health state, followed by MI, minor stroke, and major bleeding. The minimum risk reduction required to take aspirin was greater for MI prevention compared with stroke prevention. For the two clinical situations in which the hypothetical efficacy of aspirin to prevent stroke was varied, treatment thresholds for the PTOT versus DA approaches differed (p < 0.04), but this difference was not significant (p = 0.19) for the MI-based clinical situation. Using the best estimate of the efficacy of aspirin to prevent first-time stroke and MI, PTOT and DA treatment recommendations whether or not to take aspirin were discordant for 38% of participants (16 of 42) (p < 0.001). CONCLUSIONS: Patient preference-based group-level treatment thresholds and individual-level treatment recommendations can differ significantly depending on whether PTOT or DA is used, apparently because the two emphasize different aspects of the decision-making process. DA theory assumes that effective therapeutic decision making should maximize both quality and quantity of life; with PTOT, the emphasis for effective clinical decision making allows patients to be fully engaged in the process, thus hopefully leading to fully informed decisions that may result in satisfaction and compliance.
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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 | 0.051 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".