Does Introducing Imprecision around Probabilities for Benefit and Harm Influence the Way People Value Treatments?
Bibliographic record
Abstract
BACKGROUND: Imprecision in estimates of benefits and harms around treatment choices is rarely described to patients. Variation in sampling error between treatment alternatives (e.g., treatments have similar average risks, but one treatment has a larger confidence interval) can result in patients failing to choose the option that is best for them. The aim of this study is to use a discrete choice experiment to describe how 2 methods for conveying imprecision in risk influence people's treatment decisions. METHODS: We randomized a representative sample of the Canadian general population to 1 of 3 surveys that sought choices between hypothetical treatments for rheumatoid arthritis based on different levels of 7 attributes: route and frequency of administration, chance of benefit, serious and minor side effects and life expectancy, and imprecision in benefit and side-effect estimates. The surveys differed in the way imprecision was described: 1) no imprecision, 2) quantitative description based on a range with a visual graphic, and 3) qualitative description simply describing the confidence in the evidence. RESULTS: The analyzed data were from 2663 respondents. Results suggested that more people understood imprecision when it was described qualitatively (88%) versus quantitatively (68%). Respondents who appeared to understand imprecision descriptions placed high value on increased precision regarding the actual benefits and harms of treatment, equivalent to the value placed on the information about the probability of serious side effects. Both qualitative and quantitative methods led to small but significant increases in decision uncertainty for choosing any treatment. Limitations included some issues in defining understanding of imprecision and the use of an internet survey of panel members. CONCLUSIONS: These findings provide insight into how conveying imprecision information influences patient treatment choices.
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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 | 0.131 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".