{"id":"W3210097349","doi":"10.2196/32005","title":"Medical Needs Extraction for Breast Cancer Patients from Question and Answer Services: Natural Language Processing-Based Approach","year":2021,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Health, Labour and Welfare","keywords":"Relevance (law); Information needs; Computer science; Thematic analysis; Service (business); Breast cancer; Information retrieval; Natural language processing; Artificial intelligence; Medicine; Cancer; Psychology; World Wide Web; Qualitative research; Sociology","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":[0.001628049,0.00164126,0.0007360956,0.00903104,0.001241789,0.001581922,0.0009416909,0.001904636,0.005415338],"category_scores_gemma":[0.006259815,0.0005766323,0.001711784,0.003393566,0.0006060973,0.00265955,0.001771945,0.001583539,0.002686907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001686154,"about_ca_system_score_gemma":0.002981882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006005229,"about_ca_topic_score_gemma":0.009401774,"domain_scores_codex":[0.9977233,0.0006193707,0.0004568966,0.0005586481,0.0004370824,0.0002048003],"domain_scores_gemma":[0.9946882,0.003307203,0.000525027,0.0002068461,0.001107133,0.0001655743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001126415,0.001323395,0.024472,0.008197256,0.0002385325,0.005801809,0.01353522,0.00537521,0.1140077,0.008975441,0.07735475,0.7395922],"study_design_scores_gemma":[0.0005059234,0.001378514,0.1109,0.001913052,0.001064979,0.008889542,0.04045215,0.346191,0.0991658,0.04267992,0.3463585,0.000500476],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3283868,0.004016391,0.5222091,0.007197267,0.0005260149,0.009681496,0.09057187,0.01554317,0.02186799],"genre_scores_gemma":[0.2468515,0.0009501597,0.6565639,0.0007575492,0.0002486354,0.002995494,0.0857712,0.0002931663,0.005568407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00903104,"threshold_uncertainty_score":0.01811618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01800451693124583,"score_gpt":0.4401115837458187,"score_spread":0.4221070668145729,"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."}}