The effect of qualitative vs. quantitative presentation of probability estimates on patient decision‐making: a randomized trial
Bibliographic record
Abstract
BACKGROUND: Given the greater uncertainty surrounding probability estimates associated with qualitative (use of words or phrases) descriptions, the use of quantitative (numerical) information to communicate the risks and benefits of therapies is recommended but the impact of its use in decision aids is unexplored. OBJECTIVE: Using stroke prevention in atrial fibrillation as an example, to compare the impact of quantitative vs. qualitative descriptions of probability risk estimates in decision aids on the clinical decision-making process. DESIGN: Randomized trial with a 2 x 2 factorial design. SUBJECTS: A total of 198 volunteers aged 60-80 years. SETTING: Outpatient clinics of a university-affiliated, tertiary-care teaching hospital. METHODS: Participants were asked to imagine that they had atrial fibrillation, and using a decision aid, were then randomized to two ways of receiving pertinent risk information regarding the probability of stroke and major bleeding when taking warfarin, aspirin or no therapy: (1) quantitatively, in which the 2-year probabilities of stroke and major haemorrhage were presented both numerically and graphically with 100 faces (e.g. 8 of 100), and (2) qualitatively in which these probabilities were presented with the use of verbal phrases (e.g. very low, moderate). OUTCOME MEASURES: Primary: decisional conflict. Secondary: participants' choices, knowledge and expectations of outcomes using qualitative and quantitative scales. RESULTS: Participants reviewing quantitative risk information scored better on the informed subscale of the decisional conflict scale (P < 0.05) and, as expected, were better able to estimate numerically their chance of stroke and bleeding when taking warfarin, aspirin or no medication. For the low risk arm, there were no significant differences in treatment choices for the qualitative and quantitative groups. For the moderate risk arm, treatment choices between the two groups were significantly different (P = 0.01), with those in the quantitative group more likely to make an actual choice and to choose therapies at the extremes of effectiveness (warfarin and no treatment). There were no significant differences between the quantitative and qualitative groups in their ability to rank-order their stroke risk when taking warfarin, aspirin and no treatment, overall knowledge about atrial fibrillation and its treatment, and other dimensions of decisional conflict (all P-values >0.05). CONCLUSIONS: For participants without the disease in question, this study found that providing sufficient quantitative risk information makes them feel more informed, which sometimes affects their treatment choices. Further studies are necessary to confirm these findings for patients making actual clinical decisions.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".