{"id":"W2740216362","doi":"10.2196/medinform.7779","title":"What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer","year":2017,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Mental Health via Writing","field":"Psychology","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Breast cancer; Latent Dirichlet allocation; Quality of life (healthcare); Topic model; Jaccard index; Cancer; Public health; Computer science; Medicine; Psychology; Artificial intelligence; World Wide Web; Pathology; Internal medicine; Nursing; Cluster analysis","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":[0.00453312,0.0007200419,0.0006707841,0.01455479,0.0009692537,0.002021389,0.0005564598,0.0009040349,0.00165309],"category_scores_gemma":[0.01442406,0.0001939023,0.001481033,0.006254129,0.0005118204,0.001799586,0.001243958,0.0008209003,0.0007833056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008652218,"about_ca_system_score_gemma":0.0006232986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004064274,"about_ca_topic_score_gemma":0.004489606,"domain_scores_codex":[0.9969411,0.001444397,0.0003192728,0.0006491384,0.0004376633,0.0002084031],"domain_scores_gemma":[0.9794787,0.01690665,0.001642414,0.0006086208,0.0009453471,0.0004183787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001551046,0.0007099674,0.3944055,0.002500607,0.0009254155,0.0008895026,0.008158049,0.01269211,0.01014064,0.004647243,0.02290648,0.5404735],"study_design_scores_gemma":[0.0001055617,0.0004723899,0.5416115,0.0006533682,0.0006422722,0.002074927,0.009041184,0.3801138,0.005216789,0.01725022,0.04258084,0.0002372788],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8486628,0.01468923,0.09382534,0.003806876,0.0006397357,0.001332088,0.02766382,0.001632369,0.007747849],"genre_scores_gemma":[0.9315157,0.001706335,0.05052824,0.000176958,0.0008463644,0.0008439436,0.01301167,0.00008108304,0.001289698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01455479,"threshold_uncertainty_score":0.02397376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05341996232095738,"score_gpt":0.424928659255484,"score_spread":0.3715086969345266,"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."}}