{"id":"W2914040497","doi":"10.1373/clinchem.2018.289694","title":"Mass Spectrometry-Based Tissue Imaging: The Next Frontier in Clinical Diagnostics?","year":2019,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital","funders":"'s Heeren Loo","keywords":"Context (archaeology); Mass spectrometry imaging; MALDI imaging; Pathology; Gold standard (test); Tissue sample; Computational biology; Biological tissue; Biomolecule; Computer science; Biomedical engineering; Medicine; Mass spectrometry; Chemistry; Biology; Radiology; Nanotechnology; Materials science; Chromatography; Matrix-assisted laser desorption/ionization","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":[0.001166494,0.0003246931,0.000627636,0.00003543486,0.00007174971,0.0001092458,0.001111092,0.0004706653,0.01911877],"category_scores_gemma":[0.001360705,0.0002646129,0.000403974,0.0004137407,0.0003597907,0.00007321269,0.0001786138,0.001758147,0.0004224109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001129549,"about_ca_system_score_gemma":0.0001488204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001168334,"about_ca_topic_score_gemma":0.000001348926,"domain_scores_codex":[0.9966185,0.00006071718,0.001502748,0.0009024121,0.000356107,0.0005595257],"domain_scores_gemma":[0.9951874,0.002361414,0.0003771904,0.001746961,0.0000766916,0.0002503408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000549787,0.0007569086,0.9520815,0.0001478605,0.00003711014,0.0000354632,0.000004278439,0.000002844498,0.02039675,0.0006401007,0.02068493,0.005157287],"study_design_scores_gemma":[0.004816337,0.0001302929,0.04680467,0.0003261654,0.0001782148,0.00002493365,0.0002605378,0.004865863,0.2611537,0.0155239,0.6642504,0.001665051],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.784542,0.001324551,0.001149247,0.00514535,0.0003444008,0.0004007164,0.00006436671,0.0004067563,0.2066226],"genre_scores_gemma":[0.9863893,0.0006788923,0.005171879,0.0007869804,0.0009272902,0.0001036705,0.0000885375,0.00006206127,0.005791411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9052768,"threshold_uncertainty_score":0.9999806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03160580419346143,"score_gpt":0.3526003908500421,"score_spread":0.3209945866565807,"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."}}