{"id":"W2980788621","doi":"10.1039/c9an01144g","title":"Feature engineering applied to intraoperative<i>in vivo</i>Raman spectroscopy sheds light on molecular processes in brain cancer: a retrospective study of 65 patients","year":2019,"lang":"en","type":"article","venue":"The Analyst","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; GDI Integrated Facility Services (Canada); Polytechnique Montréal; McGill University; Centre Hospitalier de l’Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Interpretability; Raman spectroscopy; In vivo; Feature (linguistics); Cancer; Chemistry; Nuclear magnetic resonance; Medicine; Computer science; Internal medicine; Biology; Optics; Artificial intelligence; Physics; Genetics; Philosophy","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":[0.0001967247,0.0001696951,0.0002696348,0.0001301221,0.00001915466,0.00001543124,0.000399273,0.0001052751,0.00004375474],"category_scores_gemma":[0.0001233045,0.0001187718,0.00003857207,0.0007976035,0.00003195824,0.000002828806,0.0001490765,0.0002737837,0.000002753212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001090294,"about_ca_system_score_gemma":0.0000524171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008370639,"about_ca_topic_score_gemma":0.0002210541,"domain_scores_codex":[0.9987393,0.00003866558,0.000184418,0.000409124,0.0003388605,0.0002896196],"domain_scores_gemma":[0.9994184,0.00001949681,0.00004637038,0.000372653,0.00007778587,0.00006531857],"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":[0.0002640841,0.0004200429,0.02649323,0.00003357342,0.00004907302,0.000002627145,0.0002872614,0.0001561209,0.9713997,0.00007359071,0.0007815807,0.0000391356],"study_design_scores_gemma":[0.0006488792,0.001246245,0.006352872,0.00005308112,0.000008241572,1.70773e-7,0.0001338977,0.00003311249,0.9900582,0.00003500946,0.001286754,0.0001435821],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968446,0.00008073847,0.00007819662,0.0008089215,0.00002317573,0.0007416075,0.00001104402,0.000009512542,0.001402188],"genre_scores_gemma":[0.999136,0.00004183557,0.0001366822,0.0002829457,0.00004186003,0.0001575053,0.00001143415,0.00001860897,0.0001731337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02014036,"threshold_uncertainty_score":0.4843373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003031177324837868,"score_gpt":0.274802103528819,"score_spread":0.2717709262039811,"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."}}