Picture This: Presenting Longitudinal Patient-Reported Outcome Research Study Results to Patients
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome (PRO) results from clinical trials and research studies can inform patient-clinician decision making. However, data presentation issues specific to PROs, such as scaling directionality (higher scores may represent better or worse outcomes) and scoring strategies (normed v. nonnormed scores), can make the interpretation of PRO scores uniquely challenging. OBJECTIVE: To identify the association of PRO score directionality, score norming, and other factors on a) how accurately PRO scores are interpreted and b) how clearly they are rated by patients, clinicians, and PRO researchers. METHODS: We electronically surveyed adult cancer patients/survivors, oncology clinicians, and PRO researchers and conducted one-on-one cognitive interviews with patients/survivors and clinicians. Participants were randomized to 1 of 3 line graph formats showing longitudinal change: higher scores indicating "better," "more" (better for function, worse for symptoms), or "normed" to a population average. Quantitative data evaluated interpretation accuracy and clarity. Online survey comments and cognitive interviews were analyzed qualitatively. RESULTS: The Internet sample included 629 patients, 139 clinicians, and 249 researchers; 10 patients and 5 clinicians completed cognitive interviews. "Normed" line graphs were less accurately interpreted than "more" (odds ratio [OR] = 0.76; P = 0.04). "Better" line graphs were more accurately interpreted than both "more" (OR = 1.43; P = 0.01) and "normed" (OR = 1.88; P = 0.04). "Better" line graphs were more likely to be rated clear than "more" (OR = 1.51; P = 0.05). Qualitative data informed interpretation of these findings. LIMITATIONS: The survey relied on the online platforms used for distribution and consequent snowball sampling. We do not have information regarding participants' numeracy/graph literacy. CONCLUSIONS: For communicating PROs as line graphs in patient educational materials and decision aids, these results support using graphs, with higher scores consistently indicating better outcomes.
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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 | 0.040 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.070 | 0.028 |
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".