{"id":"W2139544932","doi":"10.4155/bio.12.250","title":"<i>In Vivo</i> Solid-Phase Microextraction For Tissue Bioanalysis","year":2012,"lang":"en","type":"review","venue":"Bioanalysis","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bioanalysis; In vivo; Solid-phase microextraction; Nanotechnology; Chemistry; Sample preparation; Biochemical engineering; Chromatography; Biocompatible material; Biomedical engineering; Materials science; Biotechnology; Mass spectrometry; Biology; Medicine; Gas chromatography–mass spectrometry","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":[],"consensus_categories":[],"category_scores_codex":[0.0004489866,0.001148152,0.0008512241,0.001987854,0.0003277383,0.000881497,0.0008380265,0.00102985,0.007127091],"category_scores_gemma":[0.0003201554,0.0003044675,0.0004781054,0.002273143,0.0004810592,0.001489705,0.000555526,0.001585463,0.009700783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006581621,"about_ca_system_score_gemma":0.0005866966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000727289,"about_ca_topic_score_gemma":0.001347951,"domain_scores_codex":[0.9997151,0.00003495201,0.00001884128,0.00005774264,0.000143273,0.00003008773],"domain_scores_gemma":[0.9998181,0.00005219778,0.00003339989,0.00001337628,0.00006691425,0.00001611665],"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":[0.00007228236,0.0001280674,0.000187902,0.01412622,0.0000663966,0.0003221127,0.00005892716,0.0006370971,0.08153088,0.009531069,0.05888056,0.8344585],"study_design_scores_gemma":[0.000007985593,0.0000923987,0.0004488117,0.0005790335,0.00003019456,0.000947602,0.0000269676,0.0002682029,0.02530602,0.001763667,0.9705074,0.00002174878],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001423929,0.9631504,0.008318666,0.0009279762,0.001662027,0.00006537892,0.0002444165,0.0001787029,0.02402844],"genre_scores_gemma":[0.008574021,0.9613872,0.007181155,0.0009984153,0.0007421775,0.00007977798,0.0004064898,0.00004022476,0.02059056],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.007127091,"threshold_uncertainty_score":0.02384245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08074335308277965,"score_gpt":0.4412389807519767,"score_spread":0.3604956276691971,"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."}}