Enhancing Readability of Lay Abstracts and Summaries for Urologic Oncology Literature Using Generative Artificial Intelligence: BRIDGE-AI 6 Randomized Controlled Trial
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
PURPOSE To evaluate a generative artificial intelligence (GAI) framework for creating readable lay abstracts and summaries (LASs) of urologic oncology research, while maintaining accuracy, completeness, and clarity, for the purpose of assessing their comprehension and perception among patients and caregivers. METHODS Forty original abstracts (OAs) on prostate, bladder, kidney, and testis cancers from leading journals were selected. LASs were generated using a free GAI tool, with three versions per abstract for consistency. Readability was compared with OAs using validated metrics. Two independent reviewers assessed accuracy, completeness, and clarity and identified AI hallucinations. A pilot study was conducted with 277 patients and caregivers randomly assigned to receive either OAs or LASs and complete comprehension and perception assessments. RESULTS Mean GAI-generated LAS generation time was <10 seconds. Across 600 sections generated, readability and quality metrics were consistent ( P > .05). Quality scores ranged from 85% to 100%, with hallucinations in 1% of sections. The best test showed significantly better readability (68.9 v 25.3; P < .001), grade level, and text metrics compared with the OA. Methods sections had slightly lower accuracy (85% v 100%; P = .03) and trifecta achievement (82.5% v 100%; P = .01), but other sections retained high quality (≥92.5%; P > .05). GAI-generated LAS recipients scored significantly better in comprehension and most perception-based questions ( P < .001) with LAS being the only consistently significant predictor ( P < .001). CONCLUSION GAI-generated LASs for urologic oncology research are highly readable and generally preserve the quality of the OAs. Patients and caregivers demonstrated improved comprehension and more favorable perceptions of LASs compared with OAs. Human oversight remains essential to ensure the accurate, complete, and clear representations of the original research.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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