{"id":"W2064147938","doi":"10.2147/ijn.s71811","title":"Surface-enhanced Raman spectroscopy of saliva proteins for the noninvasive differentiation of benign and malignant breast tumors","year":2015,"lang":"en","type":"article","venue":"International Journal of Nanomedicine","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"Program for Changjiang Scholars and Innovative Research Team in University; Canadian Institutes of Health Research; National Natural Science Foundation of China","keywords":"Saliva; Raman spectroscopy; Surface-enhanced Raman spectroscopy; Materials science; Pathology; Cancer research; Medicine; Raman scattering; Internal medicine; Optics","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":[0.0004228328,0.00008062626,0.0001876685,0.00005870629,0.00001398964,0.000006848404,0.0003296448,0.00006026958,0.00001739994],"category_scores_gemma":[0.0003579734,0.00004811229,0.00007396279,0.00005837374,0.0002574902,0.000005787155,0.00008025329,0.00009134154,1.094491e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003253403,"about_ca_system_score_gemma":0.0001373593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001863609,"about_ca_topic_score_gemma":0.000002549082,"domain_scores_codex":[0.998877,0.00002376953,0.0003914155,0.0001044587,0.0004945237,0.0001088341],"domain_scores_gemma":[0.9985762,0.00007138158,0.0003572154,0.0001023431,0.0008025724,0.00009032527],"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":[0.000776074,0.00009611809,0.001144504,0.00002220037,0.0001373556,0.000002773413,0.00006371075,0.000003783859,0.9960608,0.0001297208,0.0009490483,0.0006139342],"study_design_scores_gemma":[0.001243303,0.001338247,0.00186339,0.0001536647,0.0000240668,0.00009641331,0.0001120037,0.00005795071,0.9935762,0.0009158234,0.0005727449,0.00004617785],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500412,0.0006644349,0.04699379,0.001785641,0.0001652221,0.0001985408,0.00004175349,0.000001850785,0.0001075438],"genre_scores_gemma":[0.9954872,0.0004373881,0.003540851,0.000050493,0.0003580737,0.000005504544,0.00002345288,0.00000780534,0.00008924936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04544596,"threshold_uncertainty_score":0.1961962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564411883034095,"score_gpt":0.3222841322986257,"score_spread":0.3066400134682847,"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."}}