{"id":"W3167654141","doi":"10.2196/25621","title":"Analysis of Population Differences in Digital Conversations About Cancer Clinical Trials: Advanced Data Mining and Extraction Study","year":2021,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Stand Up To Cancer","keywords":"Clinical trial; Ethnic group; Population; Medicine; Data extraction; Psychology; MEDLINE; Pathology; Sociology; Political science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004203258,0.00009084769,0.0008545332,0.000206732,0.00003851729,0.00004110833,0.0001220424,0.0001769733,0.0003622782],"category_scores_gemma":[0.02327868,0.00007651761,0.0001042354,0.0008839754,0.0001165218,0.0002784174,0.0001548168,0.0005389189,5.258543e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000938412,"about_ca_system_score_gemma":0.0004638843,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001138918,"about_ca_topic_score_gemma":0.03099027,"domain_scores_codex":[0.9972119,0.0003431975,0.00117744,0.0005316344,0.0005939871,0.000141888],"domain_scores_gemma":[0.9880927,0.01048692,0.0003590259,0.0005652879,0.0003920438,0.000104007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002787702,0.0004226257,0.9024034,0.0000553874,0.0006652974,0.000009212606,0.0004740691,0.00002663292,0.00006286515,0.00001957313,0.00002824343,0.09555389],"study_design_scores_gemma":[0.00185274,0.0001216319,0.9879921,0.0002270033,0.0007637863,3.187899e-7,0.002494692,0.006283684,0.000009653289,0.00009116835,0.00009275552,0.0000704442],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968535,0.001001799,0.00005111842,0.001003943,0.000226815,0.0005197532,0.0002251394,0.00001107993,0.0001068278],"genre_scores_gemma":[0.9956563,0.003143759,0.0001857426,0.00007764478,0.0001399107,0.0001231919,0.0002146327,0.000008908701,0.0004499438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09548344,"threshold_uncertainty_score":0.9866917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7992562615352641,"score_gpt":0.7213653593883921,"score_spread":0.077890902146872,"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."}}