MP24-06 DEVELOPMENT AND ACCEPTABILITY OF A NOVEL GIST-BASED DECISION AID FOR PROSTATE CANCER SCREENING
Bibliographic record
Abstract
You have accessJournal of UrologyHealth Services Research: Practice Patterns, Quality of Life and Shared Decision Making II (MP24)1 May 2024MP24-06 DEVELOPMENT AND ACCEPTABILITY OF A NOVEL GIST-BASED DECISION AID FOR PROSTATE CANCER SCREENING Sunny Nalavenkata, Oskar Bergengren, Kathleen Lynch, Nicholas Emard, Mia Austria, Sené Martin, Gabriel Ogbennaya, Khadra Dualeh, Kristina Stevanovic, Jason Gonsky, Andrew Vickers, Angela Fagerlin, Jada G. Hamilton, Jennifer Hay, and Sigrid Carlsson Sunny NalavenkataSunny Nalavenkata , Oskar BergengrenOskar Bergengren , Kathleen LynchKathleen Lynch , Nicholas EmardNicholas Emard , Mia AustriaMia Austria , Sené MartinSené Martin , Gabriel OgbennayaGabriel Ogbennaya , Khadra DualehKhadra Dualeh , Kristina StevanovicKristina Stevanovic , Jason GonskyJason Gonsky , Andrew VickersAndrew Vickers , Angela FagerlinAngela Fagerlin , Jada G. HamiltonJada G. Hamilton , Jennifer HayJennifer Hay , and Sigrid CarlssonSigrid Carlsson View All Author Informationhttps://doi.org/10.1097/01.JU.0001008860.46052.c4.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The 2023 AUA/SUO guideline on early detection of prostate cancer recommends shared decision making and the use of a decision aid. However, the guideline does not indicate a specific tool to use. Traditional approaches to decision making focus on complete information and quantitative detail. In contrast, a novel approach emphasizing the main point of the message or 'gist' would be innovative, yet no such decision aid exists for prostate cancer screening. The objective of this study was to develop a novel gist-based decision aid for prostate cancer screening and to test its acceptability. METHODS: We followed a gold standard systematic development process (Ottawa Decision Support Framework, International Patient Decision Aid Standards). This included scoping and design by a multidisciplinary team, prototype development, and pilot testing in cognitive debriefing interviews to refine the content. Participants (men ages 45-60) were recruited from Kings County Hospital and NIH's ResearchMatch, a national health volunteer registry. Thematic coding and content analysis of the interview transcripts was performed by qualitative methods experts. RESULTS: A total of 40 men were interviewed in 3 rounds, with themes to establish content validity of the final decision aid (Figure 1). Median age was 55 and the majority were highly educated, with high numeracy and literacy skills. Distribution of race/ethnicity was: 53% White, 28% Black, 10% Asian; 8% Hispanic or Latino. Acceptability was high. Two thirds found the length and amount of information of the decision aid was optimal. The majority found the decision aid visually appealing, easy to read and get through, without losing interest or high mental effort. One quarter said the tool made them feel somewhat nervous, and three quarters not at all. Men could understand and relate to the images, graphs and patient stories in the decision aid. All found the decision aid helpful when making their decision and would recommend it to a friend or relative. CONCLUSIONS: We confirmed acceptability and content validity of a novel gist-based decision aid for prostate cancer screening. The next step is to assess the decision aid in clinical practice and conduct a randomized controlled trial testing the gist-based tool versus a traditional tool to determine efficacy on the quality of decision making. Download PPT Source of Funding: NIH/NCI K22-CA234400 © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e394 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Sunny Nalavenkata More articles by this author Oskar Bergengren More articles by this author Kathleen Lynch More articles by this author Nicholas Emard More articles by this author Mia Austria More articles by this author Sené Martin More articles by this author Gabriel Ogbennaya More articles by this author Khadra Dualeh More articles by this author Kristina Stevanovic More articles by this author Jason Gonsky More articles by this author Andrew Vickers More articles by this author Angela Fagerlin More articles by this author Jada G. Hamilton More articles by this author Jennifer Hay More articles by this author Sigrid Carlsson More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.018 |
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".