{"id":"W3200557289","doi":"10.2196/29958","title":"Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study","year":2021,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"USC Norris Comprehensive Cancer Center; National Institutes of Health; Southern California Clinical and Translational Science Institute; National Center for Advancing Translational Sciences; University of Southern California","keywords":"Cancer; Medicine; Social media; Clinical trial; Colorectal cancer; Breast cancer; Lung cancer; Family medicine; Cancer survivor; Prostate cancer; Gerontology; Oncology; Internal medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0621701,0.0008182239,0.0009555557,0.001582441,0.002645862,0.003010312,0.001991604,0.001569893,0.005469026],"category_scores_gemma":[0.09543502,0.0009798085,0.001516333,0.001246049,0.00164098,0.00329166,0.003233764,0.001758437,0.001589651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002878333,"about_ca_system_score_gemma":0.01373938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009101773,"about_ca_topic_score_gemma":0.01572704,"domain_scores_codex":[0.9473913,0.04329334,0.003541361,0.00177261,0.002429334,0.001572093],"domain_scores_gemma":[0.8686774,0.07604188,0.01704252,0.01204448,0.01852741,0.00766635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.05113079,0.08067136,0.4930296,0.008924359,0.002448693,0.001187433,0.02879403,0.003842531,0.00237682,0.002853523,0.03957331,0.2851675],"study_design_scores_gemma":[0.07342614,0.1552476,0.6113139,0.004733771,0.003582382,0.000715977,0.03756168,0.0306521,0.004498714,0.004219661,0.0731409,0.0009071766],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8964155,0.0005113089,0.006343756,0.004042043,0.0002271267,0.08144867,0.005406752,0.0002947291,0.005310095],"genre_scores_gemma":[0.7236527,0.0006871556,0.03636232,0.002895041,0.0002605391,0.2317107,0.003087322,0.00006177863,0.001282335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0621701,"threshold_uncertainty_score":0.3287908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9229272852342361,"score_gpt":0.748581104089987,"score_spread":0.174346181144249,"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."}}