{"id":"W3089756171","doi":"10.2196/25337","title":"Correction: A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Baidu","keywords":"Radiomics; Particle swarm optimization; Support vector machine; Computer science; Lymph node metastasis; Degree (music); Node (physics); Medicine; Metastasis; Artificial intelligence; Machine learning; Cancer; Internal medicine; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.006542757,0.002852656,0.002880662,0.003741574,0.002137727,0.003821397,0.004961923,0.00852504,0.0381241],"category_scores_gemma":[0.09355104,0.001360637,0.002672954,0.002480774,0.003178829,0.00206512,0.001889992,0.01151647,0.01732188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003324894,"about_ca_system_score_gemma":0.005682515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0141478,"about_ca_topic_score_gemma":0.01444626,"domain_scores_codex":[0.9923571,0.001373412,0.001624088,0.001046597,0.002984229,0.000614539],"domain_scores_gemma":[0.9537274,0.01178217,0.002079152,0.002556918,0.0279709,0.001883525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000111248,0.000008939441,0.0002683416,0.0004727366,0.00007618755,0.0006844176,0.0000637577,0.0001454758,0.0001496406,0.001020911,0.988521,0.008477462],"study_design_scores_gemma":[0.0002185406,0.00008840455,0.003399988,0.0009632602,0.0002946248,0.005100321,0.0002826537,0.003083531,0.001465763,0.004136963,0.9807851,0.0001807525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.000315808,0.001339327,0.002006423,0.04324858,0.9507278,0.00002906128,0.001208152,0.0004659614,0.0006588394],"genre_scores_gemma":[0.03566221,0.008847066,0.0125226,0.08673549,0.7687627,0.0003313419,0.002568542,0.002097924,0.08247218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0381241,"threshold_uncertainty_score":0.1275378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03569403278292189,"score_gpt":0.3005518222846627,"score_spread":0.2648577895017408,"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."}}