{"id":"W2091704306","doi":"10.1038/labinvest.2014.44","title":"Low invasive in vivo tissue sampling for monitoring biomarkers and drugs during surgery","year":2014,"lang":"en","type":"article","venue":"Laboratory Investigation","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; Toronto General Hospital; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Waterloo","keywords":"Ex vivo; Perfusion; In vivo; Lung; Medicine; Biomedical engineering; Metabolite; Pathology; Pharmacology; Biology; Radiology; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0002453053,0.0001229667,0.0001520889,0.0001289739,0.0001184851,0.00005008556,0.00007134889,0.0001072171,0.00005411359],"category_scores_gemma":[0.0002848304,0.0001441022,0.00002041238,0.0003311583,0.00005868551,0.000153954,0.00002444569,0.00009103222,0.000001285566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009018814,"about_ca_system_score_gemma":0.00005295187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003409294,"about_ca_topic_score_gemma":0.00001261545,"domain_scores_codex":[0.9991832,0.00001812819,0.0002467367,0.0002679673,0.00009781268,0.0001861719],"domain_scores_gemma":[0.9991751,0.0003381967,0.000125454,0.0001995689,0.00007128602,0.00009036753],"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":[0.000005549758,0.00001007306,0.06190665,0.0003477309,0.000007096084,2.845911e-7,0.0001498745,0.000007796726,0.9354061,0.001240148,0.00007335997,0.0008452935],"study_design_scores_gemma":[0.0001566186,0.000006057333,0.004756756,0.0001995616,0.000007041592,5.442596e-7,0.0001379739,0.00009825861,0.9895918,0.003735232,0.001150535,0.000159653],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99876,0.00005604526,0.0002958745,0.0002001312,0.00003994559,0.0001355129,0.0000329475,0.000136964,0.0003425596],"genre_scores_gemma":[0.9930044,0.00003565669,0.006411862,0.00003480479,0.0001855966,0.0002425002,0.00002238833,0.00002472997,0.00003809141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0571499,"threshold_uncertainty_score":0.5876318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01590576128272136,"score_gpt":0.2573209455358598,"score_spread":0.2414151842531384,"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."}}