{"id":"W4390942579","doi":"10.5334/ijic.icic23616","title":"Lessons learned from screening literature on person-centred care: Exploration of text-mining functions for semi-automatization of study identification.","year":2023,"lang":"en","type":"article","venue":"International Journal of Integrated Care","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Terminology; Identification (biology); Health care; Citation; Presentation (obstetrics); Systematic review; Process (computing); Knowledge base; Computer science; Knowledge management; Data science; MEDLINE; Psychology; Medicine; World Wide Web; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.351532,0.002850257,0.005332122,0.03735878,0.003743115,0.01987319,0.007030728,0.002953888,0.007723188],"category_scores_gemma":[0.6797078,0.002481303,0.005004105,0.03284772,0.005272981,0.01686957,0.01228504,0.004690699,0.004600999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006535443,"about_ca_system_score_gemma":0.01946882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003640732,"about_ca_topic_score_gemma":0.008320797,"domain_scores_codex":[0.6717595,0.2441766,0.05283945,0.008397602,0.02132235,0.001504479],"domain_scores_gemma":[0.1058089,0.8051659,0.02572186,0.02836222,0.0333184,0.001622632],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007431133,0.0002103122,0.004998292,0.05182463,0.0007969858,0.0009018698,0.04765662,0.001845112,0.00672233,0.01410793,0.03113899,0.8390538],"study_design_scores_gemma":[0.001524385,0.001534497,0.03660227,0.1255731,0.00371953,0.005699062,0.07325631,0.05926945,0.0370766,0.2708844,0.3827145,0.002145867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03598082,0.02858565,0.8303924,0.04171431,0.0017672,0.02302007,0.01004269,0.01964885,0.008848057],"genre_scores_gemma":[0.03829714,0.003827867,0.9399024,0.00135749,0.0002993867,0.01154857,0.002635225,0.0009512178,0.001180689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.648468,"threshold_uncertainty_score":0.7996765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1677602527417911,"score_gpt":0.4576216114713269,"score_spread":0.2898613587295358,"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."}}