{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003557638,0.0001630951,0.001233116,0.000281142,0.00004488923,0.00000286507,0.0002351756,0.00006362603,0.00009927979],"category_scores_gemma":[0.003953145,0.0001295454,0.0001218927,0.0001588612,0.0008908955,0.0001236119,0.000163827,0.0003208494,0.0000768656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002094591,"about_ca_system_score_gemma":0.0001234727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000863733,"about_ca_topic_score_gemma":0.000001766551,"domain_scores_codex":[0.9966623,0.0008948331,0.001835527,0.000119491,0.0001142305,0.0003736701],"domain_scores_gemma":[0.9967875,0.0006290479,0.001594331,0.0003267237,0.000379071,0.0002833216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004201237,0.00005078621,0.9841555,0.0002247792,0.0002471632,0.0002541099,0.000578987,0.000009521848,0.0001261073,0.0009978544,0.007148927,0.005786085],"study_design_scores_gemma":[0.002986841,0.002303095,0.9064864,0.0005415489,0.0001159002,0.06150464,0.0002869153,0.0004391889,0.00007773359,0.0001865115,0.02489595,0.0001752888],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918192,0.001242491,0.0039114,0.0006231663,0.0005229469,0.00007668423,0.00001311873,0.00002162514,0.001769359],"genre_scores_gemma":[0.982681,0.001185087,0.01541934,0.0006221607,0.00003580834,2.640628e-7,0.000007359091,0.00002301174,0.00002600496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07766917,"threshold_uncertainty_score":0.5282708,"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."}}