Multi-model LLM assessment of Quality Control Circlemethodological quality: a designed-anchor reliabilitystudy

BackgroundQuality Control Circle (QCC) reports are often reviewed qualitatively, but reviewer workload and inter-rater variability make large-scale assessment difficult. We evaluated whether multiple large language models (LLMs) could score QCC methodological quality reliably on a designed-anchor benchmark. ObjectiveTo estimate inter-model reliability for QCC quality scoring and to assess whether model scores align with designed synthetic anchors and remain descriptively comparable to a small set of public PMC QCC reports. MethodsWe evaluated 30 synthetic QCC reports and 8 public PMC QCC reports across four primary evaluators (GPT, Gemini, Grok, DeepSeek) and one sensitivity evaluator (Claude); Claude was excluded from the primary panel because it shared the model family used during prompt development. Each synthetic case was scored across eight QCC quality dimensions in three runs per evaluator. We summarized each evaluator by median scores, then estimated ICC(A,1) across the primary panel. We also examined score-based calibration against designed anchors, keyword-assisted defect mention, leave-one-out and k=5 sensitivity, and a descriptive synthetic-versus-PMC distributional plausibility check. ResultsInter-model reliability on the primary k=4 panel was excellent: ICC(A,1) = 0.953 (95% CI 0.944 to 0.962) with 237 pooled case-dimension rows. The pre-specified k=5 sensitivity analysis including Claude was 0.954, and leave-one-out estimates within the primary panel ranged from 0.950 to 0.959. Score-based calibration against designed anchors met the prespecified target in 57/58 trap-affected case-dimension rows (98.3%). Keyword-assisted defect mention was present in 51/58 trap instances (87.9%). The synthetic-versus-PMC comparison was descriptively similar across all eight dimensions, and all dimensions met the predefined descriptive margin check. ConclusionsIn this designed-anchor pilot, multi-model LLM scoring of QCC methodological quality showed high inter-model reliability and stable alignment with synthetic anchor scores. These findings support benchmark feasibility, but they do not establish expert validity, clinical validity, or operational deployment readiness.