{"id":"W7125826300","doi":"10.21428/594757db.aa7d2deb","title":"Improving Patient-Clinical Trial Matching Using Convolution Neural Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Interpretability; Cosine similarity; Matching (statistics); Similarity (geometry); Rank (graph theory); Artificial neural network; Convolution (computer science); Process (computing)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007918071,0.0001295215,0.0002144949,0.0001019285,0.000277304,0.0001940793,0.0005263524,0.000134038,0.00001314431],"category_scores_gemma":[0.0003113844,0.0001187873,0.000108869,0.00037416,0.00003232655,0.0003304651,0.0005153444,0.0005733021,0.000004559206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008215463,"about_ca_system_score_gemma":0.0001430348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001255574,"about_ca_topic_score_gemma":0.00003515163,"domain_scores_codex":[0.9978882,0.0004693092,0.0006303126,0.0004693056,0.0001890129,0.0003538426],"domain_scores_gemma":[0.9987032,0.0004317203,0.0001925107,0.0005004211,0.00008342262,0.00008868678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008450627,0.0001029896,0.0160853,0.0000592474,0.0000250662,0.00001573247,0.0002698088,0.09298652,0.0000378174,0.04530104,0.0003687107,0.8439027],"study_design_scores_gemma":[0.00337136,0.000166191,0.001238763,0.00002323179,0.000005445173,0.00000387237,0.00002112823,0.9943032,0.000004294725,0.0006010629,0.0001504978,0.0001109629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.278325,0.00008010965,0.716017,0.0006240279,0.003904027,0.0002862495,1.453077e-7,0.0002516893,0.0005118235],"genre_scores_gemma":[0.9455276,0.000001694502,0.05255792,0.001500883,0.0002829932,0.000006672941,0.000001512959,0.000007126793,0.0001136706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9013166,"threshold_uncertainty_score":0.4844004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04882367028939942,"score_gpt":0.3747894137489948,"score_spread":0.3259657434595953,"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."}}