{"id":"W2168267005","doi":"10.1186/1559-0275-8-11","title":"Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer","year":2011,"lang":"en","type":"article","venue":"Clinical Proteomics","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Center for Research Resources; National Cancer Institute; Medisinske fakultet, Universitetet i Oslo; Aarhus Universitetshospital; H. Lundbeck A/S; Lundbeckfonden; Susan G. Komen for the Cure; University of Texas MD Anderson Cancer Center; American Society of Clinical Oncology; Kræftens Bekæmpelse; Universitetet i Oslo; Strategiske Forskningsråd; Aarhus Universitet; Norges Forskningsråd","keywords":"Breast cancer; Medicine; Taxane; Oncology; Biomarker; Internal medicine; Cancer; Logistic regression; Recursive partitioning; Biology","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":[0.0003146461,0.0001483662,0.000173562,0.000027902,0.00006867998,0.000009245606,0.0001022025,0.0001864127,0.00000451989],"category_scores_gemma":[0.0001078052,0.0001169248,0.00004120059,0.00008351441,0.0002836155,0.000004612822,0.0001209985,0.000194275,0.000001031586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002043695,"about_ca_system_score_gemma":0.00008348154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002047307,"about_ca_topic_score_gemma":0.00003561727,"domain_scores_codex":[0.9988749,0.00009420862,0.000329443,0.0004326604,0.00007615794,0.0001926492],"domain_scores_gemma":[0.9993427,0.0000206411,0.0001483441,0.0002604685,0.0001405534,0.00008729317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007569563,0.0005483208,0.9655699,0.00001342558,0.00002629075,0.000003331507,0.0000140021,0.000008590792,0.02355412,0.00008250687,0.0001812942,0.002428648],"study_design_scores_gemma":[0.0013028,0.001100517,0.9912361,0.0000317052,0.00001503772,0.00001097064,0.000005742994,0.0000402502,0.005528686,0.0002997991,0.0002392888,0.000189119],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932714,0.00001675967,0.004778225,0.0004127128,0.00003020594,0.001181189,0.0002583997,0.00002892842,0.00002217873],"genre_scores_gemma":[0.9376506,0.00006537883,0.06147574,0.0002995929,0.00006299131,0.000301507,0.00009520187,0.00002166245,0.00002730409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05669752,"threshold_uncertainty_score":0.4768056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04383567638091088,"score_gpt":0.2871618860051764,"score_spread":0.2433262096242655,"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."}}