{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008763599,0.0006615253,0.0005416953,0.0008887512,0.000289936,0.0004356756,0.0005497605,0.0006735162,0.0006941057],"category_scores_gemma":[0.001123712,0.0002891722,0.0004559461,0.0003864062,0.0003600516,0.0004006726,0.0004689412,0.0006026869,0.0003715995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002156017,"about_ca_system_score_gemma":0.0003489472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003168399,"about_ca_topic_score_gemma":0.0009701562,"domain_scores_codex":[0.9991727,0.0003264502,0.0000352694,0.0001220447,0.0003066485,0.00003691545],"domain_scores_gemma":[0.9994919,0.0002660654,0.00007445236,0.00003905993,0.00009890454,0.00002956307],"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":[0.0002140959,0.0001004206,0.004162597,0.0003418494,0.00003897906,0.0001547963,0.0000693646,0.0004982881,0.9210103,0.0002996652,0.0002747668,0.07283475],"study_design_scores_gemma":[0.00003886307,0.00108236,0.01762761,0.00003363498,0.00008985655,0.002507152,0.0001021163,0.023157,0.95026,0.0007530746,0.004282283,0.00006613391],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5356023,0.01708223,0.4396262,0.0009548793,0.0003095433,0.0004125298,0.0003761432,0.001312407,0.004323787],"genre_scores_gemma":[0.6823018,0.005190554,0.3100958,0.0002503685,0.0001044323,0.000163164,0.0001756611,0.00004129487,0.001676933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008887512,"threshold_uncertainty_score":0.004634738,"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."}}