{"id":"W1996774767","doi":"10.1016/j.jchromb.2010.12.020","title":"A validated enantioselective assay for the simultaneous quantitation of (R)-, (S)-fluoxetine and (R)-, (S)-norfluoxetine in ovine plasma using liquid chromatography with tandem mass spectrometry (LC/MS/MS)","year":2010,"lang":"en","type":"article","venue":"Journal of Chromatography B","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Child and Family Research Institute; Eli Lilly and Company","keywords":"Chemistry; Chromatography; Tandem mass spectrometry; Analyte; Selected reaction monitoring; Detection limit; Liquid chromatography–mass spectrometry; Fluoxetine; Mass spectrometry; High-performance liquid chromatography; Extraction (chemistry)","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001019319,0.0005358011,0.001028711,0.0008573678,0.0002314014,0.00009567503,0.0005381925,0.0003837991,0.000204757],"category_scores_gemma":[0.0005394413,0.0003811121,0.0006489693,0.00229264,0.0008494625,0.0003020733,0.00005031238,0.001014819,4.142882e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003603148,"about_ca_system_score_gemma":0.0001885733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005140048,"about_ca_topic_score_gemma":0.00007232754,"domain_scores_codex":[0.9966777,0.0000756201,0.001399583,0.0004637541,0.0007633228,0.0006200229],"domain_scores_gemma":[0.9951946,0.001820955,0.001519759,0.0004468394,0.000743381,0.0002744413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001790463,0.0004893738,0.04637157,0.0005682847,0.001044059,0.0001057502,0.0002352631,0.0002901746,0.9485299,0.0003969907,0.00005820614,0.0001199746],"study_design_scores_gemma":[0.007046582,0.001449885,0.003648084,0.000927267,0.00117454,0.001505594,0.001233486,0.01297142,0.9677608,0.000887791,0.0005546335,0.0008399198],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899871,0.000863168,0.007962031,0.0001452912,0.0001010896,0.0002616916,0.00005995905,0.00004583066,0.0005738296],"genre_scores_gemma":[0.9872099,0.0002391376,0.01215473,0.00002587685,0.0002560418,0.00001367951,0.00001738867,0.00007344246,0.000009776148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04272348,"threshold_uncertainty_score":0.9998641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008732208996318777,"score_gpt":0.2470164362540316,"score_spread":0.2382842272577128,"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."}}