Assessing demographic variation in large language model outputs for patient education materials in cataract surgery
Bibliographic record
Abstract
• Demographic factors influence LLM readability and word count in cataract education • Indigenous and Black prompts elicited the most complex LLM-generated responses • Insured patients consistently received longer, more complex content across models • Geographic disparities across Canadian provinces were observed, with Nunavut receiving the least readable outputs. • No LLM met AMA readability standards; all exceeded sixth grade reading level To evaluate whether large language model (LLM)-generated patient education materials for cataract surgery vary in readability, length, and accuracy based on demographic modifiers including race, gender, geography, and insurance status. Cross-sectional study This study analyzed 7,000 responses from five LLMs (ChatGPT, Claude, Copilot, DeepSeek, and Gemini) between March-May 2025 using 280 standardized prompts that varied by race, gender, province/territory, and insurance coverage. Each prompt was submitted five times. Readability was assessed using the Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease (FRE), and SMOG index. Accuracy was assessed by dual blinded reviewers against AAO clinical guidelines. ANOVA was performed (α = 0.05). LLM outputs differed significantly across all metrics (p < 0.001). Gemini generated the longest (876 ± 143 words) and most complex text (FKGL 15.2 ± 0.8). Race, insurance status, and geography significantly impacted readability. Prompts referencing Indigenous patients were the most complex (FKGL 11.1 ± 1.8, FRE 36.5 ± 7.9). Insured prompts were longer and more complex (11.0 ± 1.7 vs. 10.8 ± 1.7; 429 vs. 399 words; p < 0.001). Prompts from Nunavut and Manitoba were the least readable (FKGL ≥ 11.1), while Quebec and PEI were most readable. Gender had minimal impact. No outputs contained clinically unsafe information, but most lacked sufficient depth. None of the responses met the AMA’s sixth-grade readability recommendation. LLM-generated patient education for cataract surgery varies by patient demographics. These disparities may hinder equitable access to health information and highlight the need for bias-aware development of AI tools in healthcare.
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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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".