{"id":"W1832475695","doi":"10.1186/bcr1193","title":"Magnetic resonance spectroscopy of breast cancer tissue used for tumor classification and lymph node prediction","year":2005,"lang":"en","type":"article","venue":"Breast Cancer Research","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston Health Sciences Centre","funders":"Regione Marche; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Associazione Italiana per la Ricerca sul Cancro; National Cancer Institute; KWF Kankerbestrijding; Fondation de France; Norges Forskningsråd; Cancer Research UK; Kreftforeningen; Ministerstvo Zdravotnictví Ceské Republiky; Morris Animal Foundation; Deutsche Forschungsgemeinschaft; Universitetet i Oslo; Macquarie University; Breast Cancer Campaign; Kræftens Bekæmpelse; Instituto de Salud Carlos III; Florida State University; National Institutes of Health; U.S. Department of Health and Human Services; U.S. Department of Defense","keywords":"Surgical oncology; Breast cancer; Medicine; Lymph node; Pathology; Magnetic resonance imaging; Cancer; Oncology; Radiology; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0003006367,0.000130821,0.0002345111,0.0001576512,0.0001556047,0.00001810855,0.0001231226,0.00007615545,0.0002611572],"category_scores_gemma":[0.00001589625,0.0001203859,0.00003782338,0.0004188224,0.0002607715,0.0001176966,0.00004467195,0.0001944323,0.000005283895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000435607,"about_ca_system_score_gemma":0.0003307153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007267325,"about_ca_topic_score_gemma":0.0001104055,"domain_scores_codex":[0.9984454,0.00003228899,0.0002849873,0.0004201587,0.0004251252,0.0003920664],"domain_scores_gemma":[0.9987946,0.00006636636,0.00008104197,0.0003740196,0.0005266532,0.0001573692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001301193,0.0003163212,0.03533271,0.0002963047,0.00001136716,0.000001540329,0.0001388957,0.00003976913,0.6671789,0.002072456,0.0047982,0.2885123],"study_design_scores_gemma":[0.002371245,0.000235998,0.8512462,0.0005616779,0.00005826974,0.0002060675,0.0001292747,0.007263562,0.08060719,0.0005341934,0.05660193,0.0001843387],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8439215,0.01805485,0.02730428,0.09086928,0.0001520494,0.008181124,0.009166655,0.0004701669,0.001880035],"genre_scores_gemma":[0.9758546,0.002867661,0.01585558,0.000140762,0.0007433216,0.003102624,0.00004171713,0.00005369735,0.00134001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8159136,"threshold_uncertainty_score":0.4909193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.047434028590686,"score_gpt":0.4197763735547578,"score_spread":0.3723423449640718,"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."}}