{"id":"W2792275468","doi":"10.5152/eurjrheum.2018.17105","title":"Infodemiology of antiphospholipid syndrome: Merging informatics and epidemiology","year":2018,"lang":"en","type":"article","venue":"European Journal of Rheumatology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Informatics; Epidemiology; Health informatics; Antiphospholipid syndrome; Globe; Clinical trial; MEDLINE; Public health; Family medicine; Data science; Pathology; Computer science; Internal medicine","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":[0.001835342,0.000286055,0.0004392126,0.01197504,0.0002884785,0.001993211,0.0003410685,0.0004784493,0.001739064],"category_scores_gemma":[0.007865699,0.0001328943,0.0006583651,0.0137191,0.0003917991,0.002373531,0.00102088,0.0004326552,0.0002790297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007431571,"about_ca_system_score_gemma":0.001388171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004943728,"about_ca_topic_score_gemma":0.005445234,"domain_scores_codex":[0.9984949,0.0005818285,0.0002766653,0.0002248118,0.0003324042,0.00008947052],"domain_scores_gemma":[0.9925895,0.002596485,0.003313295,0.0002994437,0.000835195,0.0003662366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001080356,0.00006572189,0.9229078,0.001150921,0.0003275182,0.0002118722,0.0003168315,0.0002457156,0.000296847,0.001572542,0.001922437,0.07087368],"study_design_scores_gemma":[0.00001170626,0.0001271806,0.9832404,0.001301521,0.0003055061,0.001250453,0.001119059,0.001734557,0.0003129929,0.001977502,0.00860144,0.00001769833],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8612352,0.09170432,0.007489879,0.008178979,0.0003542802,0.0002338676,0.01496905,0.0001772552,0.01565712],"genre_scores_gemma":[0.9612092,0.02759711,0.005120052,0.0004794902,0.0005215129,0.00008624788,0.004369207,0.00001373814,0.0006035063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01197504,"threshold_uncertainty_score":0.009829879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02178373504883462,"score_gpt":0.2893226858028037,"score_spread":0.2675389507539691,"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."}}