{"id":"W4398153731","doi":"10.2196/51347","title":"Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis","year":2024,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Radiogenomics; Bibliometrics; Data science; Computational biology; Biology; Computer science; Library science; Radiomics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.005913706,0.0006269406,0.001214362,0.1240184,0.0005919802,0.004618922,0.000701147,0.0006496697,0.002629953],"category_scores_gemma":[0.02946288,0.0002026951,0.002231572,0.2016002,0.0004692432,0.00301141,0.001524874,0.0004738103,0.0008286724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002819986,"about_ca_system_score_gemma":0.003428419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009787632,"about_ca_topic_score_gemma":0.008160195,"domain_scores_codex":[0.9928738,0.0009393993,0.001672747,0.000746437,0.003413205,0.0003543073],"domain_scores_gemma":[0.9702745,0.01260986,0.009719354,0.0007603437,0.00592398,0.0007120195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000233042,0.00007877289,0.7786356,0.01073685,0.002746871,0.0005947675,0.002131519,0.005144496,0.0009086223,0.004389612,0.01907019,0.1753297],"study_design_scores_gemma":[0.00002700775,0.0001122572,0.9183007,0.001980455,0.001605388,0.001130467,0.002764178,0.008586482,0.0009940361,0.002409495,0.06200052,0.00008897774],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6603359,0.1050332,0.00588091,0.005334395,0.0003830277,0.0004346488,0.1919499,0.00105251,0.02959541],"genre_scores_gemma":[0.8982896,0.03089085,0.005997944,0.0002563153,0.000460185,0.0004338919,0.06162208,0.0001111186,0.001938113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8759816,"threshold_uncertainty_score":0.03127503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05894001489551383,"score_gpt":0.5003711779207107,"score_spread":0.4414311630251969,"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."}}