{"id":"W3160942152","doi":"","title":"急性期主幹動脈閉塞に対する血栓回収術のlearning curve～初期導入以降のstent retrieverとADAPTの治療成績の変化～","year":2016,"lang":"ja","type":"article","venue":"The Japanese Society for Neuroendovascular Therapy","topic":"Pharmacy and Medical Practices","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Labrador Retriever; Learning curve; Medicine; Computer science; Surgery","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":[0.002867092,0.0004635413,0.0004519949,0.00122112,0.000831216,0.001564619,0.0007379695,0.0005460066,0.02029018],"category_scores_gemma":[0.01081222,0.0002304247,0.0007566405,0.0007411378,0.0008455125,0.001885412,0.0007922469,0.0007205753,0.008787574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009465407,"about_ca_system_score_gemma":0.002119219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002320529,"about_ca_topic_score_gemma":0.003014448,"domain_scores_codex":[0.9984263,0.0001851293,0.0001548174,0.0002333182,0.0008623959,0.0001379515],"domain_scores_gemma":[0.9876993,0.005027235,0.001196169,0.0008110714,0.004325634,0.0009406243],"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":[0.0008071507,0.001074905,0.08907527,0.0002897234,0.00007028611,0.0001827651,0.0007567056,0.001449526,0.006049988,0.002451707,0.004215607,0.8935764],"study_design_scores_gemma":[0.0005740777,0.009001723,0.6409069,0.001008066,0.0009995768,0.009147688,0.006071792,0.02067957,0.1283816,0.03004499,0.1527103,0.0004736599],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7726371,0.005261375,0.064799,0.003128733,0.0003780008,0.0007766858,0.0008508264,0.001237851,0.1509304],"genre_scores_gemma":[0.9012727,0.004948271,0.04038047,0.001207517,0.0003236044,0.0003308507,0.00109066,0.0003182873,0.05012772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02029018,"threshold_uncertainty_score":0.06787735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1459382467331098,"score_gpt":0.4084561739830403,"score_spread":0.2625179272499305,"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."}}