{"id":"W2948997694","doi":"10.1101/667915","title":"Multi-objective optimisation of material properties and strut geometry for poly(L-lactic acid) coronary stents using response surface methodology","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"Engineering and Physical Sciences Research Council; Agenzia Nazionale per le Nuove Tecnologie, l'Energia e lo Sviluppo Economico Sostenibile; University of Warwick; California Institute of Technology","keywords":"Stent; Finite element method; Materials science; Parametric statistics; Response surface methodology; Stiffness; Computer science; Structural engineering; Biomedical engineering; Composite material; Surgery; Medicine; Mathematics; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001121394,0.0004153432,0.0008656595,0.0003371602,0.0000964585,0.00005662997,0.0001498415,0.0005204938,0.00004517962],"category_scores_gemma":[0.001711871,0.0004114622,0.0001929321,0.0001790634,0.0001801501,0.0001334435,0.0003822881,0.0003449706,0.000003597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000325769,"about_ca_system_score_gemma":0.0007075732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001268207,"about_ca_topic_score_gemma":0.000001410618,"domain_scores_codex":[0.997578,0.0004646993,0.000661301,0.0007020938,0.0002378683,0.0003560561],"domain_scores_gemma":[0.997326,0.000446981,0.0005843461,0.0007120938,0.0007790157,0.0001516342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006969174,0.0003643854,0.02641219,0.001939016,0.0004553668,0.00002368888,0.00004224249,0.000122471,0.9636264,0.00001601508,0.00001167151,0.00001734772],"study_design_scores_gemma":[0.002443777,0.0008959715,0.4133433,0.001292913,0.0007217662,5.668916e-7,0.0000664984,0.004181684,0.5766082,7.042067e-7,0.00004160812,0.0004030694],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982439,0.003947334,0.009000001,0.00006686031,0.001824606,0.001821624,0.0008189841,0.00008125277,3.112812e-7],"genre_scores_gemma":[0.8649035,0.00009049639,0.1346733,0.00003905323,0.000117724,0.00006795951,0.000001902958,0.00009178377,0.00001425786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3870183,"threshold_uncertainty_score":0.9998337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1007940961322826,"score_gpt":0.3107484924620744,"score_spread":0.2099543963297917,"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."}}