Related Experiment Video
Updated: May 29, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Detecting Citation Veneer in Guideline-Grounded Clinical and Public Health Large Language Model (LLM) Outputs: A
1Public Policy, Hosei University, Tokyo, JPN.
Abstract:
Large language model (LLM) systems are increasingly used to summarize clinical guidelines, draft patient-facing education, and answer evidence-linked clinical or public health questions. We define citation veneer as an observable audit state in which an output presents citation cues or apparently supportive source information while still containing incorrect, incomplete, or materially unsupported content. We develop and evaluate the Clinical Evidence Audit Grid (CEAG), an analyst-facing monitoring surface that encodes six binary response-quality signals into a fixed-length plain-text audit tag (CENHOV): clinical correctness (C), evidential support (E), numeric concordance (N), hedging (H), stylistic ornamentation (O), and a derived CEAG-V supported-error citation veneer marker (V=1 when E=1 and C=0). CEAG was evaluated on a controlled benchmark (synthetic, n=120 questions per condition) and on a 12-case public check built from PubHealth-style health-claim examples and public medical or public-health sources. The controlled benchmark used scripted response regimes rather than a live LLM/API benchmark; therefore, model identity, sampling temperature, and seed are not applicable to the main controlled analysis. In the synthetic benchmark, the RUSH regime retained high evidential-support rates (93.3%, 95% CI 87.4%-96.6%) while dropping to 73.3% correctness (95% CI 64.8%-80.4%) and producing 25.8% CEAG-V veneer (95% CI 18.8%-34.3%). In the 12-case public check, RUSH produced 66.7% CEAG-V veneer (95% CI 39.1%-86.2%), whereas VERIFY and POSTER produced none; because n=12, these public-case rates are descriptive pilot data only. CEAG is not proposed as a patient-facing display or a replacement for conventional charts. It is a clinical informatics monitoring aid for reviewers who need to detect apparently well-sourced hallucinations, compare response regimes, and separate stylistic change from substantive trust change.
Related Concept Videos
Standards of Care II
Clinical Trials: Overview
Guidelines for Writing Outcome
Patient outcomes reflect the patient's response to the goal rather than what the nurse aims to achieve. Terminology should be observable and measurable to avoid the reader's interpretation. The desired outcome should be realistic and achievable in the designated care timeframe. Expected outcomes should align with adjunctive therapies. The outcome should enhance care evaluation by...
Specialized Care Centers and Settings-II
Rural health centers are specialized care facilities in remote locations with very few medical personnel. The primary care providers who run the centers are mostly Registered Nurse Practitioners. Here, emergency treatment is provided to critically ill or injured patients before they are transferred to the closest hospital. Fortunately, due to advancement in technology, many rural healthcare facilities and professionals have easy access to diagnostic and treatment...
Guidelines for Nursing Documentation II
Timely documentation is crucial to ensure continuity of care for patients. Any delays in recording or reporting medical information can result in medical errors and even adverse patient outcomes. From medication administration to diagnostic test results, every detail must be accurately and promptly documented to provide the best possible care for patients.
Guidelines for Nursing Documentation I
Factual:
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.