Perception of Quantitative Information for Treatment Decisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it