{"id":"W1988284258","doi":"10.1007/s10278-014-9678-z","title":"A Content-Boosted Collaborative Filtering Algorithm for Personalized Training in Interpretation of Radiological Imaging","year":2014,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Computer science; Collaborative filtering; Metric (unit); Interpretation (philosophy); Algorithm; Personalized medicine; Content (measure theory); Machine learning; Data mining; Artificial intelligence; Recommender system; Bioinformatics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003820324,0.001391382,0.003002066,0.002502439,0.001456804,0.001395179,0.00398416,0.004102812,0.003099008],"category_scores_gemma":[0.008636001,0.001168243,0.002216679,0.002162031,0.0009050299,0.001804618,0.002235137,0.002500757,0.001988932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001002259,"about_ca_system_score_gemma":0.002227101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02009398,"about_ca_topic_score_gemma":0.02411856,"domain_scores_codex":[0.9975528,0.0005113354,0.0001726283,0.000837133,0.0006441479,0.0002819024],"domain_scores_gemma":[0.9951541,0.002645449,0.0002031051,0.000485579,0.001299031,0.0002127196],"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":[0.0006598207,0.0005894374,0.002020908,0.0001528189,0.0002786704,0.0001930181,0.00026115,0.1477436,0.0159229,0.002293221,0.007072911,0.8228115],"study_design_scores_gemma":[0.0000257533,0.00007403144,0.0004805177,0.00001049124,0.0000482455,0.0001136228,0.00001739258,0.9926534,0.004135877,0.001438912,0.0009834092,0.00001831006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007129578,0.0003399689,0.9905877,0.0001301579,0.00007559248,0.00005936611,0.00006584289,0.001177877,0.0004338157],"genre_scores_gemma":[0.1701712,0.0003627749,0.8233227,0.0003615844,0.0002505209,0.0002331467,0.0006355473,0.0002632728,0.004399371],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02009398,"threshold_uncertainty_score":0.03995401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02200589460111775,"score_gpt":0.3047266604658938,"score_spread":0.2827207658647761,"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."}}