Characterizing the Impact of Novel Patient-Centered Pathology Reports on Men Undergoing Prostate Biopsy: The Patient-Centered Pathology Report Randomized Controlled Trial
Bibliographic record
Abstract
PURPOSE: To improve patient-centered communication, some institutions provide online portals for patients to immediately access medical results. However, early access to certain reports could cause patients stress and confusion. Standard pathology reports (SPRs), vital for clinical decision-making, can be difficult for patients to understand and contextualize. Our group designed patient-centered pathology reports (PAPRs) and tested them for patients undergoing prostate biopsy. MATERIALS AND METHODS: Between February 2023 and January 2024, 121 men were randomly assigned to receive either SPRs or SPRs + PAPRs. Before follow-up encounters, participants completed a questionnaire examining their understanding of the reports and experience interacting with them and a validated anxiety questionnaire (STAIS-5). After the visit, patients completed a validated 9-item shared decision-making questionnaire. RESULTS: < .001). Anxiety increased less in the SPR + PAPR arm. CONCLUSIONS: When comparing PAPRs with SPRs, we did not see evidence of an impact on shared decision-making, but there were statistically significant differences in patient understanding and experience. Generalizability is limited by high resources needed to create PAPRs.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".