{"id":"W4386250690","doi":"10.24908/iqurcp16750","title":"Natural Language Processing of Radiology Reports: Predicting Metastatic Progression from Text Data","year":2023,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Readability; Context (archaeology); Natural language processing; Artificial intelligence; Sentence; Information retrieval; Unified Medical Language System; SNOMED CT; Radiology; Medical physics; Medicine; Terminology; Linguistics; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004641042,0.0002937479,0.0005202328,0.0007174963,0.0003624875,0.0005926724,0.003088826,0.0001330543,0.000007852494],"category_scores_gemma":[0.002472013,0.0002525658,0.00005951929,0.002160746,0.0005449185,0.002371795,0.003476232,0.0009941384,0.00002779896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001126572,"about_ca_system_score_gemma":0.0008037522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005305835,"about_ca_topic_score_gemma":0.00001370042,"domain_scores_codex":[0.9944381,0.0002107865,0.0009097256,0.00157114,0.001783882,0.001086384],"domain_scores_gemma":[0.9962983,0.0005310441,0.0004977288,0.001191844,0.001250658,0.0002304257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001535952,0.0003290611,0.02811319,0.001876479,0.0003835903,0.001179173,0.07804404,0.00008605698,0.09879996,0.02988353,0.01311216,0.7480392],"study_design_scores_gemma":[0.0004904581,0.0001769801,0.001170255,0.001096815,0.00002462043,0.0001568453,0.009017124,0.9315367,0.00524544,0.05032307,0.0003358385,0.0004258703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7831197,0.002852315,0.1834506,0.0233447,0.001229315,0.00209191,0.00004161928,0.002088239,0.001781668],"genre_scores_gemma":[0.9689473,0.0001700807,0.03019997,0.00003267575,0.0002390699,0.00007794305,0.0001019837,0.00003345276,0.0001975315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9314506,"threshold_uncertainty_score":0.9999927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1647811326748784,"score_gpt":0.4224478813233055,"score_spread":0.2576667486484271,"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."}}