{"id":"W2002568502","doi":"10.1039/c3an36890d","title":"Blood plasma surface-enhanced Raman spectroscopy for non-invasive optical detection of cervical cancer","year":2013,"lang":"en","type":"article","venue":"The Analyst","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":181,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"Canadian Institutes of Health Research","keywords":"Surface-enhanced Raman spectroscopy; Linear discriminant analysis; Principal component analysis; Raman spectroscopy; Cancer detection; Cervical cancer; Blood plasma; Spectroscopy; Chemistry; Cancer; Analytical Chemistry (journal); Materials science; Chromatography; Medicine; Internal medicine; Artificial intelligence; Raman scattering; Optics; Computer science","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":[0.00015914,0.0001308533,0.0001957427,0.0000248643,0.00007715821,0.00001993644,0.0003662066,0.0001740053,0.0001503207],"category_scores_gemma":[0.0001284111,0.0000862235,0.0001586592,0.0001702238,0.0002936209,0.000003502113,0.000124978,0.0001495001,0.000009784031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002192497,"about_ca_system_score_gemma":0.00006589398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001677245,"about_ca_topic_score_gemma":0.00008768794,"domain_scores_codex":[0.9989152,0.00002711864,0.0002271257,0.0002822784,0.0002220996,0.0003261923],"domain_scores_gemma":[0.9992179,0.00008070088,0.00007031149,0.000347153,0.0001731803,0.0001107558],"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":[0.000115525,0.0000783204,0.0001447492,0.00003464925,0.0001279363,2.490004e-7,0.0000108042,0.000004190647,0.9978671,0.00004141053,0.001093008,0.0004820536],"study_design_scores_gemma":[0.0003980477,0.0004919406,0.0003068841,0.00001691022,0.00007076734,0.000001884673,0.00003402726,0.0003587902,0.9970239,0.0005464587,0.0006432363,0.0001071279],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9789287,0.0001199054,0.01883179,0.0005323996,0.00004366085,0.0003820985,0.00001529083,0.00001384254,0.001132311],"genre_scores_gemma":[0.9959664,0.0003470444,0.002614654,0.0001098151,0.0002404303,0.0001711465,0.00002890136,0.00001653473,0.0005051138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01703766,"threshold_uncertainty_score":0.3516093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00852877527458947,"score_gpt":0.2989270559933637,"score_spread":0.2903982807187743,"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."}}