{"id":"W2120254749","doi":"10.3174/ajnr.a4110","title":"MRI Texture Analysis Predicts p53 Status in Head and Neck Squamous Cell Carcinoma","year":2014,"lang":"en","type":"article","venue":"American Journal of Neuroradiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; University of Calgary","funders":"","keywords":"Medicine; Head and neck squamous-cell carcinoma; Radiology; Head and neck cancer; Carcinoma; Pathology; Oncology; Cancer; 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":[0.0004258469,0.0001629186,0.0009288987,0.0005989186,0.00003603437,0.00001297676,0.0001282884,0.00005508465,0.0000208451],"category_scores_gemma":[0.0005698851,0.000127017,0.0001488847,0.0006249554,0.0004068449,0.00004899454,0.00003386735,0.0008947191,0.000001557636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000043158,"about_ca_system_score_gemma":0.00009433575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002209481,"about_ca_topic_score_gemma":0.0000136399,"domain_scores_codex":[0.9982983,0.000378123,0.0005062368,0.0002435796,0.0001849522,0.0003888089],"domain_scores_gemma":[0.9984782,0.0003845615,0.0004351518,0.0002078228,0.00007622376,0.0004180441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000234796,0.00009163219,0.9745819,0.00002185952,0.00006572687,0.0004443269,0.0004084577,0.001102913,0.001272269,0.00001940077,0.0004788931,0.02127781],"study_design_scores_gemma":[0.002528577,0.004294703,0.9622164,0.00001657907,0.0004417124,0.001736083,0.0001522133,0.0222722,0.00002175735,0.00003892435,0.006167722,0.0001131663],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924552,0.0004398302,0.002474844,0.003804869,0.0001525617,0.00006743633,0.00000183366,0.000009915416,0.0005935226],"genre_scores_gemma":[0.9945328,0.000188517,0.002097402,0.00286521,0.0002523412,0.00000101832,0.000004442729,0.00002140172,0.00003683025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02116928,"threshold_uncertainty_score":0.5179603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00577654971546891,"score_gpt":0.2612694031756125,"score_spread":0.2554928534601436,"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."}}