Perception of Quantitative Information for Treatment Decisions
Bibliographic record
Abstract
The study was designed to determine which formats for displaying quantities, such as probabilities of treatment risks and benefits, are perceived most accurately and easily by patients. Accuracy and speed of processing were compared for six different presentation formats: pie charts, vertical bars, horizontal bars, numbers, systematic ovals, and random ovals. Quantities were used in two tasks: a choice task that required larger/smaller judgments and an estimate task that required more precise evaluation. The impacts of blue-yellow color and of a treatment-decision context on performance in the two tasks were also investigated. The study included four experiments. Taken together the results suggest that the formats best for making a choice differ from those best for estimating the size of an amount. For making a choice, vertical bars, horizontal bars, numbers, and systematic ovals were equally well perceived; pie charts and random ovals caused slower and less accurate performances. For estimating, numbers led to the most accurate estimates, followed by systematic ovals. The other four formats led to the least accurate estimates. Color and context did not alter which formats were best.
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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 | 0.011 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".