Guideline Concordance of Large Language Model Responses to Parent-oriented Guideline Prompts About Pediatric Acute Bacterial Arthritis

BACKGROUND: Large language models (LLMs) are increasingly used by patients and caregivers to obtain medical information. In pediatric acute bacterial arthritis, non-guideline-concordant information may be clinically important because timely diagnosis and management are essential. This study evaluated the concordance of LLM-generated responses to parent-oriented reformulations of Pediatric Infectious Diseases Society/Infectious Diseases Society of America (PIDS/IDSA) guideline recommendations. METHODS: In this exploratory cross-sectional benchmarking study, 27 PIDS/IDSA guideline-derived recommendations and good practice statements were reformulated into standardized parent-oriented prompts. The same prompts were submitted to GPT-5.4 Thinking, Gemini 3 Thinking and Claude 4.6 Sonnet through browser-based interfaces on April 12, 2026. Responses were anonymized and independently assessed by 3 blinded reviewers. Each response was classified as concordant or discordant; final classifications were determined by majority decision. Interrater agreement was assessed using Fleiss' kappa, and model differences were evaluated using Cochran's Q test. RESULTS: Overall, 75 of 81 responses (92.6%) were concordant with PIDS/IDSA recommendations. Gemini 3 Thinking achieved concordance in 27/27 responses (100.0%), Claude 4.6 Sonnet in 25/27 (92.6%) and GPT-5.4 Thinking in 23/27 (85.2%). Cochran's Q test showed no significant difference among models (Q = 4.800, df = 2, P = 0.091). No unsupported or hallucinated content was identified. Interrater agreement was moderate (κ = 0.580; 95% CI, 0.454-0.706; P < 0.001). CONCLUSIONS: LLMs showed high guideline concordance, but selected item-level discordance persisted. Because these models may have been trained on guideline-derived content, high concordance should not be equated with independent clinical reasoning. These tools may support caregiver-oriented education but should not replace clinician assessment or guideline-based care.

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Journal
The Pediatric Infectious Disease Journal
Published
2026-07-28
DOI
https://doi.org/10.1097/inf.0000000000005335
Primary Topic
Clinical Reasoning and Diagnostic Skills
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article
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article

Guideline Concordance of Large Language Model Responses to Parent-oriented Guideline Prompts About Pediatric Acute Bacterial Arthritis

Ahmet Alperen Öztürk, Belen Ateş, Orkun Dinç, Ahmet Murat Çörekçi
The Pediatric Infectious Disease Journal
Clinical Reasoning and Diagnostic Skills
article

Guideline Concordance of Large Language Model Responses to Parent-oriented Guideline Prompts About Pediatric Acute Bacterial Arthritis

Ahmet Alperen Öztürk, Belen Ateş, Orkun Dinç, Ahmet Murat Çörekçi
article en

Abstract

BACKGROUND: Large language models (LLMs) are increasingly used by patients and caregivers to obtain medical information. In pediatric acute bacterial arthritis, non-guideline-concordant information may be clinically important because timely diagnosis and management are essential. This study evaluated the concordance of LLM-generated responses to parent-oriented reformulations of Pediatric Infectious Diseases Society/Infectious Diseases Society of America (PIDS/IDSA) guideline recommendations. METHODS: In this exploratory cross-sectional benchmarking study, 27 PIDS/IDSA guideline-derived recommendations and good practice statements were reformulated into standardized parent-oriented prompts. The same prompts were submitted to GPT-5.4 Thinking, Gemini 3 Thinking and Claude 4.6 Sonnet through browser-based interfaces on April 12, 2026. Responses were anonymized and independently assessed by 3 blinded reviewers. Each response was classified as concordant or discordant; final classifications were determined by majority decision. Interrater agreement was assessed using Fleiss' kappa, and model differences were evaluated using Cochran's Q test. RESULTS: Overall, 75 of 81 responses (92.6%) were concordant with PIDS/IDSA recommendations. Gemini 3 Thinking achieved concordance in 27/27 responses (100.0%), Claude 4.6 Sonnet in 25/27 (92.6%) and GPT-5.4 Thinking in 23/27 (85.2%). Cochran's Q test showed no significant difference among models (Q = 4.800, df = 2, P = 0.091). No unsupported or hallucinated content was identified. Interrater agreement was moderate (κ = 0.580; 95% CI, 0.454-0.706; P < 0.001). CONCLUSIONS: LLMs showed high guideline concordance, but selected item-level discordance persisted. Because these models may have been trained on guideline-derived content, high concordance should not be equated with independent clinical reasoning. These tools may support caregiver-oriented education but should not replace clinician assessment or guideline-based care.

The Pediatric Infectious Disease Journal
Education Training And Research (US), Sağlık Bilimleri Üniversitesi (TR), University of Health Sciences Antigua (AG)
Quality Education
Openalex Percentile: Top 6%
Clinical Reasoning and Diagnostic Skills
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