{"id":"W4402991652","doi":"10.60087/jklst.v3.n4.p213","title":"Microfluidics and personalized medicine towards diagnostic precision and treatment efficacy","year":2024,"lang":"en","type":"article","venue":"Journal of Knowledge Learning and Science Technology ISSN 2959-6386 (online)","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Personalized medicine; Precision medicine; Microfluidics; Medicine; Medical physics; Computer science; Data science; Computational biology; Nanotechnology; Bioinformatics; Biology; Pathology; Materials 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001420687,0.0002063724,0.0003972812,0.001529773,0.0002386759,0.00009660159,0.000284333,0.0002031315,0.00002560074],"category_scores_gemma":[0.005520676,0.0001414827,0.00004041874,0.001373034,0.002511133,0.0001485107,0.0001901598,0.0009554832,0.000005491385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001491199,"about_ca_system_score_gemma":0.0001922925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005207989,"about_ca_topic_score_gemma":0.000002815549,"domain_scores_codex":[0.9984454,0.00005563705,0.0004092383,0.0003194374,0.0003733423,0.0003969653],"domain_scores_gemma":[0.9981751,0.001166809,0.00005914794,0.0001228713,0.0001916773,0.0002843764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001451776,0.00007575733,0.00484051,0.0001195262,0.00005583453,0.0001237516,0.001963776,0.0000158373,0.06340431,0.0002382967,0.0003441593,0.9288037],"study_design_scores_gemma":[0.007383083,0.008975103,0.06446406,0.00932167,0.0005269028,0.006772248,0.008558596,0.1599296,0.0233684,0.003499783,0.705825,0.001375594],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9041988,0.0905909,0.0008716123,0.003471805,0.0003612992,0.00009967292,0.000002305619,0.000182989,0.0002206389],"genre_scores_gemma":[0.9630632,0.0342507,0.002045308,0.000008234148,0.0002087257,0.000002371043,0.00000116114,0.00002222746,0.0003980132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9274281,"threshold_uncertainty_score":0.9252373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02531452305919039,"score_gpt":0.3487886847348637,"score_spread":0.3234741616756733,"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."}}