{"id":"W2475251747","doi":"10.1186/s12868-016-0275-6","title":"Technical considerations of a game-theoretical approach for lesion symptom mapping","year":2016,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Bundesministerium für Bildung und Forschung","keywords":"Lesion; Artificial intelligence; Binary number; Computer science; Support vector machine; Machine learning; Binary classification; Pattern recognition (psychology); Mathematics; Medicine; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01290342,0.00104833,0.001039854,0.001343643,0.001064029,0.003410872,0.003178795,0.002060124,0.007681669],"category_scores_gemma":[0.04256384,0.0006928777,0.00164508,0.0007636826,0.002726928,0.004809887,0.003363731,0.003369766,0.000870425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002572801,"about_ca_system_score_gemma":0.002794155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002685509,"about_ca_topic_score_gemma":0.001953116,"domain_scores_codex":[0.995103,0.002914163,0.0002842179,0.0006840817,0.0007784161,0.0002361005],"domain_scores_gemma":[0.9746917,0.02141837,0.0007128347,0.001179141,0.001565638,0.0004322207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006175901,0.00007448799,0.001258627,0.0001893381,0.00008144808,0.0002148763,0.0002259999,0.2583123,0.00117539,0.7182342,0.00160556,0.01856604],"study_design_scores_gemma":[0.0000189603,0.00003746472,0.0001705635,0.00002715086,0.00001321778,0.00008763879,0.00003397102,0.5636069,0.0003323991,0.4340569,0.001598417,0.00001644774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005768617,0.00009460328,0.9889816,0.001468823,0.00003732912,0.00009670063,0.00007893597,0.0000506412,0.003422685],"genre_scores_gemma":[0.3662593,0.0004281506,0.6265802,0.0007306866,0.000252705,0.001235909,0.00026885,0.0001629739,0.004081088],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01290342,"threshold_uncertainty_score":0.06824064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1196357475719461,"score_gpt":0.3083891989797117,"score_spread":0.1887534514077656,"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."}}