{"id":"W2142632334","doi":"10.1373/clinchem.2005.064758","title":"Fast In Vivo Microextraction: A New Tool for Clinical Analysis","year":2006,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Chromatography; Solid-phase microextraction; In vivo; Chemistry; Sample preparation; Extraction (chemistry); Detection limit; Pharmacokinetics; Mass spectrometry; Pharmacology; Gas chromatography–mass spectrometry; Medicine","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00180121,0.0004394672,0.001243448,0.00004524619,0.00007754816,0.0000770341,0.0006123544,0.001046581,0.005959392],"category_scores_gemma":[0.002636302,0.0004592973,0.001480362,0.0006386457,0.0002909487,0.00008647166,0.0001614076,0.001046951,0.0000412863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002241378,"about_ca_system_score_gemma":0.0004370639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003181232,"about_ca_topic_score_gemma":0.00001697785,"domain_scores_codex":[0.9945349,0.00005606089,0.003020752,0.001321209,0.0003745196,0.000692626],"domain_scores_gemma":[0.9950054,0.002944088,0.0005256377,0.0009473795,0.0001572801,0.0004201827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006754423,0.001995309,0.8614182,0.0005628045,0.001319007,0.00009764719,0.00001182433,0.00009900593,0.05825555,0.00005546509,0.0639202,0.01158956],"study_design_scores_gemma":[0.007401387,0.00004056276,0.02063525,0.0001632586,0.002208422,0.00003355378,0.000114746,0.003571745,0.5599227,0.002620642,0.4013248,0.001962958],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8512247,0.0005154978,0.07073692,0.002517223,0.0003970659,0.0003300474,0.0001973073,0.0004290016,0.07365228],"genre_scores_gemma":[0.6418036,0.0002954432,0.1184859,0.000664527,0.009436395,0.0001750984,0.0006262521,0.0001802321,0.2283326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8407829,"threshold_uncertainty_score":0.9997859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08083979737304275,"score_gpt":0.4245738044553417,"score_spread":0.3437340070822989,"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."}}