{"id":"W3216372912","doi":"10.2196/29768","title":"A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Pipeline (software); Mental illness; Construct (python library); Computer science; Mental health; Data science; Medicine; Psychology; Psychiatry; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003017781,0.000214635,0.000537528,0.0003212492,0.001379067,0.0001556125,0.0007817389,0.0002906039,0.0005637456],"category_scores_gemma":[0.005372455,0.0002042806,0.00008574109,0.00307699,0.0002462394,0.0004402521,0.0003459833,0.0005222892,0.00007537857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004234024,"about_ca_system_score_gemma":0.001733556,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002615457,"about_ca_topic_score_gemma":0.03363538,"domain_scores_codex":[0.9949152,0.0005248679,0.00105991,0.0003022084,0.002519666,0.0006781624],"domain_scores_gemma":[0.9952987,0.002273697,0.000295579,0.0006567063,0.0004691831,0.001006173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002300137,0.0004961666,0.0007930501,0.00005853976,0.0001522078,0.00059942,0.8819029,0.000004206934,2.290981e-7,0.0004395625,0.01182562,0.1037052],"study_design_scores_gemma":[0.0005897217,0.00003436715,0.001328034,0.00004120129,0.0002679021,0.0001868947,0.9842095,0.001190385,0.000002168467,0.00009584445,0.01172969,0.0003243015],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673643,0.00002416447,0.00705788,0.01691627,0.005618556,0.001679942,0.0002212511,0.0001671045,0.0009505638],"genre_scores_gemma":[0.9942349,0.00002276505,0.0002648981,0.002577447,0.002335591,0.0002218832,0.0003055258,0.00001808542,0.00001889728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1033809,"threshold_uncertainty_score":0.999921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1296129886646236,"score_gpt":0.4495019258236717,"score_spread":0.319888937159048,"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."}}