Presenting comparative study PRO results to clinicians and researchers: beyond the eye of the beholder
Bibliographic record
Abstract
PURPOSE: Patient-reported outcome (PRO) results from clinical trials can inform clinical care, but PRO interpretation is challenging. We evaluated the interpretation accuracy and perceived clarity of various strategies for displaying clinical trial PRO findings. METHODS: We conducted an e-survey of oncology clinicians and PRO researchers (supplemented by one-on-one clinician interviews) that randomized respondents to view one of the three line-graph formats (average scores over time for two treatments on four domains): (1) higher scores consistently indicating "better" patient status; (2) higher scores indicating "more" of what was being measured (better for function, worse for symptoms); or (3) normed scores. Two formats displayed proportions changed (pie/bar charts). Multivariate modeling was used to analyze interpretation accuracy and clarity ratings. RESULTS: Two hundred and thirty-three clinicians and 248 researchers responded; ten clinicians were interviewed. Line graphs with "better" directionality were more likely to be interpreted accurately than "normed" line graphs (OR 1.55; 95% CI 1.01-2.38; p = 0.04). No significant differences were found between "better" and "more" formats. "Better" formatted graphs were also more likely to be rated "very clear" versus "normed" formatted graphs (OR 1.91; 95% CI 1.44-2.54; p < 0.001). For proportions changed, respondents were less likely to make an interpretation error with pie versus bar charts (OR 0.35; 95% CI 0.2-0.6; p < 0.001); clarity ratings did not differ between formats. Qualitative findings informed the interpretation of the survey findings. CONCLUSIONS: Graphic formats for presenting PRO data differ in how accurately they are interpreted and how clear they are perceived to be. These findings will inform the development of best practices for optimally reporting PRO findings.
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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 | 0.760 | 0.885 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".