{"id":"W6967601237","doi":"10.5281/zenodo.13789375","title":"Can a machine learning classifier pipeline detect infantile spasms in a clinical dataset?","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"","keywords":"Pipeline (software); Classifier (UML); Pattern recognition (psychology); Support vector machine; Epilepsy; Training set","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":[0.001496516,0.001279469,0.001194636,0.001324419,0.0003975804,0.001081278,0.001000384,0.002073241,0.0030383],"category_scores_gemma":[0.005172381,0.0002606321,0.001059367,0.0007273008,0.0001941317,0.0007906823,0.0007975393,0.001123416,0.003937518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003679337,"about_ca_system_score_gemma":0.0008469396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00603122,"about_ca_topic_score_gemma":0.01265844,"domain_scores_codex":[0.9994044,0.0001243212,0.00005331767,0.0002180062,0.00008798121,0.0001119575],"domain_scores_gemma":[0.99874,0.0004597481,0.00007856862,0.0002379789,0.0003413274,0.0001423177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002845663,0.001388914,0.1271181,0.0007446716,0.000790185,0.001198137,0.0001301114,0.007895561,0.04454472,0.0006205014,0.2043097,0.6084138],"study_design_scores_gemma":[0.001218253,0.002966025,0.2509352,0.0005561287,0.001284127,0.004738112,0.001021967,0.5650381,0.06028302,0.01124712,0.1004554,0.0002564446],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7610627,0.009482151,0.07281482,0.01378001,0.003389243,0.0007584842,0.110917,0.01758422,0.0102114],"genre_scores_gemma":[0.7783949,0.001394697,0.06280538,0.001971768,0.0009378347,0.0004134584,0.1478629,0.0003772425,0.005841846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00603122,"threshold_uncertainty_score":0.01199222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05490017238131355,"score_gpt":0.3307816348769326,"score_spread":0.275881462495619,"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."}}