{"id":"W2902964569","doi":"10.1016/j.artres.2018.10.212","title":"P159 CORRELATION BETWEEN INFLAMMATORY STATE AND ARTERIAL STIFFNESS","year":2018,"lang":"en","type":"article","venue":"Artery Research","topic":"Cardiac, Anesthesia and Surgical Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Arterial stiffness; Cardiology; State (computer science); Internal medicine; Blood pressure; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001027265,0.0000893846,0.0003006115,0.0001674683,0.0001594155,0.00005814031,0.00004433698,0.00007773278,0.0002416726],"category_scores_gemma":[0.0001701815,0.00007180667,0.00006516082,0.0001723477,0.0004042137,0.0001102589,0.00005420607,0.0002383768,0.0006552603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000335218,"about_ca_system_score_gemma":0.00008333801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001368627,"about_ca_topic_score_gemma":0.000005571103,"domain_scores_codex":[0.9984918,0.000215124,0.0001992899,0.0002269225,0.0005212352,0.00034559],"domain_scores_gemma":[0.998637,0.0006649506,0.00002171891,0.0002372729,0.000214633,0.0002244077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001267276,0.00005936516,0.9241747,0.00009515684,0.0001360122,0.0004382976,0.001980314,2.388057e-7,0.006207546,0.001392377,0.002627405,0.06162136],"study_design_scores_gemma":[0.001158538,0.0001972909,0.9380275,0.00003621283,0.00001847121,0.00007042105,0.00008986089,0.00007400996,0.0003060635,0.0002148212,0.0597277,0.00007909677],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878563,0.00003334773,0.00004805586,0.00105285,0.000131323,0.0002943684,0.000003460052,0.0000301676,0.01055011],"genre_scores_gemma":[0.9930048,0.00002073366,0.00003378081,0.00007914782,0.0008345674,0.0000131097,0.00001420433,0.00001505626,0.005984625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06154226,"threshold_uncertainty_score":0.8422264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05939181371904557,"score_gpt":0.3636273767284411,"score_spread":0.3042355630093955,"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."}}