{"id":"W6930778962","doi":"10.5281/zenodo.15045514","title":"TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Vibrio bacteria research studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Columbia Bible College","funders":"European Commission","keywords":"Matching (statistics); Normalization (sociology); Recommender system; Clinical trial; 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":[0.005007319,0.001301802,0.001216874,0.002457182,0.0004974254,0.001797763,0.002558963,0.002428256,0.04335245],"category_scores_gemma":[0.02388969,0.0006701018,0.001288676,0.002688048,0.0002800465,0.001139532,0.001878558,0.001733104,0.03815508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001689205,"about_ca_system_score_gemma":0.004180344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008071736,"about_ca_topic_score_gemma":0.03031681,"domain_scores_codex":[0.9975151,0.001023247,0.0004931998,0.0004828681,0.0003795599,0.0001059842],"domain_scores_gemma":[0.9908561,0.00498689,0.000681244,0.001726562,0.001116888,0.0006323538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008467541,0.0001456193,0.003303727,0.001801998,0.0002628339,0.0001640224,0.00006049545,0.002385471,0.001446742,0.001496841,0.9543085,0.03377691],"study_design_scores_gemma":[0.002856467,0.0003633731,0.006689072,0.0005348109,0.0003698087,0.0004445002,0.0001109299,0.03044922,0.007764785,0.0139609,0.9363196,0.0001366219],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002270269,0.0008081611,0.0164107,0.00149237,0.0002545559,0.0009126649,0.955543,0.01726541,0.005042916],"genre_scores_gemma":[0.008986585,0.0004304661,0.04169606,0.001267593,0.0000715763,0.001342247,0.942296,0.0008182675,0.003091222],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04335245,"threshold_uncertainty_score":0.1450284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0440621349662912,"score_gpt":0.345665723728539,"score_spread":0.3016035887622478,"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."}}