{"id":"W4417482640","doi":"10.1016/j.eswa.2025.130870","title":"Brain compensation mechanisms of large language models in clinical decision-making in acupuncture: A fusion study using fNIRS and eye-tracking","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Acupuncture Treatment Research Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Science Fund for Distinguished Young Scholars; Natural Science Foundation of Sichuan Province; Chengdu University of Traditional Chinese Medicine; National Natural Science Foundation of China","keywords":"Interpretability; Neurocognitive; Cognition; Neuroimaging; Artificial neural network; Brain activity and meditation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009755362,0.0002379652,0.0007352164,0.0004163615,0.0001165487,0.00002355355,0.00009721235,0.0001041787,0.000003514184],"category_scores_gemma":[0.0001951955,0.0001283482,0.00005264461,0.0007004183,0.00005722223,0.0001018841,0.000124768,0.0002145944,7.891549e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002265959,"about_ca_system_score_gemma":0.0001174958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005842147,"about_ca_topic_score_gemma":0.001137657,"domain_scores_codex":[0.9980971,0.0001818652,0.0006734593,0.0004420165,0.0003700038,0.0002355949],"domain_scores_gemma":[0.9983771,0.0008482257,0.0001600345,0.0004285566,0.0001320436,0.00005407533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001682946,0.005756562,0.8975734,0.0007909998,0.0006844081,0.0002067644,0.04054837,0.003019869,0.01792962,0.01488685,0.0002721865,0.01664801],"study_design_scores_gemma":[0.02038608,0.001246578,0.62196,0.009411832,0.0003850635,0.00007689104,0.1735675,0.1674008,0.0006164778,0.003688895,0.0004922922,0.0007675672],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7300653,0.001688679,0.2630838,0.0003109507,0.00003414333,0.004610053,0.000006380602,0.00003604507,0.0001646235],"genre_scores_gemma":[0.9922673,0.00003504012,0.006659559,0.0001229803,0.00003468828,0.000812748,0.00001242207,0.00001728587,0.00003795636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2756134,"threshold_uncertainty_score":0.5233889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03342507256075702,"score_gpt":0.4333932929346722,"score_spread":0.3999682203739152,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}