{"id":"W4388343734","doi":"10.1007/978-3-031-45705-0_14","title":"Design, Analysis, and Optimization of a Novel Stent Retriever for Acute Ischemic Stroke","year":2023,"lang":"en","type":"book-chapter","venue":"Mechanisms and machine science","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Stent; Labrador Retriever; Medicine; Flexibility (engineering); Surgery; Mathematics","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":[0.0002681385,0.0006178624,0.0006898413,0.0002971423,0.0002913901,0.0009032501,0.0007913264,0.0007448672,0.003342877],"category_scores_gemma":[0.0002565729,0.0003026835,0.0005561114,0.0001695287,0.0001856577,0.0003261598,0.0002284307,0.0002995583,0.001251715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000343355,"about_ca_system_score_gemma":0.0004347186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006623795,"about_ca_topic_score_gemma":0.001067518,"domain_scores_codex":[0.999856,0.00002001826,0.000006937767,0.00003876035,0.00006231978,0.00001600338],"domain_scores_gemma":[0.9998943,0.00003620278,0.00002101013,0.000008848197,0.00003367253,0.000006064655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006952769,0.0002466788,0.001731603,0.0006395973,0.0003592239,0.0007300468,0.0001045434,0.3497051,0.3250268,0.01437921,0.009352885,0.297029],"study_design_scores_gemma":[0.00008035679,0.001503721,0.002482708,0.00003391289,0.0003420289,0.0008020309,0.00003801488,0.9091874,0.05784289,0.002511078,0.02512823,0.00004767411],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05745899,0.003369811,0.9122263,0.0005949868,0.0002345264,0.0001954432,0.0003493265,0.001978842,0.0235917],"genre_scores_gemma":[0.6612234,0.002784074,0.293289,0.000449456,0.0002810299,0.0003328407,0.0004362047,0.0003287014,0.04087533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003342877,"threshold_uncertainty_score":0.01118302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03624705594768056,"score_gpt":0.2916666134253341,"score_spread":0.2554195574776535,"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."}}