{"id":"W7132062488","doi":"","title":"From lesion detection to VR exposure therapy: machine learning applications in medical contexts","year":2024,"lang":"en","type":"other","venue":"NPARC","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical imaging; Lesion; Feature (linguistics); Pattern recognition (psychology); Component (thermodynamics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001658018,0.0006756188,0.0005075451,0.001829309,0.0006131972,0.002337269,0.001166352,0.001723466,0.07023547],"category_scores_gemma":[0.01558002,0.0002826839,0.0004584228,0.0009662975,0.0004480919,0.001228993,0.001582171,0.001242398,0.02937241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003658166,"about_ca_system_score_gemma":0.00097673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002900712,"about_ca_topic_score_gemma":0.006059241,"domain_scores_codex":[0.9983606,0.0005016542,0.00005933276,0.0003145112,0.0006600336,0.0001037532],"domain_scores_gemma":[0.9939926,0.002535946,0.0002926288,0.0006739825,0.002065479,0.0004393315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002343547,0.0002734728,0.005674497,0.0003601392,0.00003648092,0.0006008297,0.0001848422,0.003178375,0.005001228,0.001578609,0.1601472,0.8227299],"study_design_scores_gemma":[0.0002171728,0.000709965,0.06083351,0.001159302,0.0001510212,0.004122811,0.001597011,0.2389678,0.03612054,0.04555309,0.6103528,0.0002149934],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.1478912,0.0132672,0.3441626,0.0258491,0.01010794,0.001657351,0.02220934,0.03797944,0.3968758],"genre_scores_gemma":[0.5854306,0.006644732,0.1769395,0.001900222,0.002791054,0.0004244454,0.01525805,0.003388769,0.2072225],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.07023547,"threshold_uncertainty_score":0.234961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655863397631425,"score_gpt":0.2942912647446668,"score_spread":0.2777326307683525,"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."}}