{"id":"W3047444657","doi":"10.1109/ecti-con49241.2020.9158244","title":"Alzheimer Screening using Drawing Test Scores","year":2020,"lang":"en","type":"article","venue":"","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Test (biology); Computer science; Point (geometry); Artificial intelligence; Classifier (UML); Computer vision; Mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001148996,0.001074144,0.0006502452,0.003504643,0.0003989249,0.0010914,0.0006476778,0.0006329426,0.009218547],"category_scores_gemma":[0.00567859,0.0001555513,0.0008053538,0.001413708,0.0002103988,0.0005783905,0.0006782099,0.0005754215,0.003241117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000367272,"about_ca_system_score_gemma":0.0003075776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003629001,"about_ca_topic_score_gemma":0.005219041,"domain_scores_codex":[0.9989404,0.0001698631,0.0001363791,0.0001468071,0.0005322493,0.00007426467],"domain_scores_gemma":[0.9984027,0.0002218135,0.0002871764,0.0001018343,0.0008560928,0.0001303735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001413629,0.001072717,0.545754,0.0003404425,0.0003185664,0.0009633405,0.0004199718,0.001676211,0.006903686,0.0007456769,0.03405562,0.4063361],"study_design_scores_gemma":[0.0002161424,0.002693788,0.9575489,0.0001643981,0.0003554435,0.004187382,0.0004188935,0.005573318,0.00916663,0.002106588,0.01743516,0.0001332581],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9006886,0.001989864,0.01504419,0.0005406608,0.0003206266,0.002455011,0.01333481,0.002352389,0.06327395],"genre_scores_gemma":[0.9410132,0.001885644,0.0238108,0.0002790013,0.0001251623,0.001298684,0.0120001,0.0000912477,0.01949614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009218547,"threshold_uncertainty_score":0.03083909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1198092172047543,"score_gpt":0.368337043561188,"score_spread":0.2485278263564336,"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."}}