{"id":"W4286295814","doi":"10.1200/jco.2022.40.16_suppl.e13587","title":"Canadian oncology residents’ knowledge of and attitudes towards artificial intelligence and machine learning.","year":2022,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Queen's University","funders":"","keywords":"Medicine; Artificial intelligence; Oncology; Radiation oncology; Thematic analysis; Curriculum; Internal medicine; Medical education; Perception; Family medicine; Psychology; Qualitative research; Radiation therapy; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001878267,0.0001618557,0.0002392991,0.001182696,0.002825317,0.0013658,0.0008349359,0.0005765862,0.01242554],"category_scores_gemma":[0.01573697,0.0002243954,0.0003509346,0.001827664,0.00127926,0.0007200509,0.001034927,0.0009397737,0.0006197492],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01407585,"about_ca_system_score_gemma":0.03929188,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9414102,"about_ca_topic_score_gemma":0.9709518,"domain_scores_codex":[0.9984962,0.0001658618,0.00007402535,0.00009159865,0.0008090182,0.0003632522],"domain_scores_gemma":[0.9878947,0.001928989,0.002002002,0.0001204677,0.003930237,0.004123557],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001173183,0.0001917735,0.8674017,0.0005205472,0.00003612724,0.0005102995,0.03108758,0.0002296161,0.0002004403,0.0005907309,0.03976011,0.05935384],"study_design_scores_gemma":[0.00001482358,0.00008097688,0.9263104,0.0005465817,0.00002106028,0.0003799016,0.04612271,0.0002778304,0.00007725541,0.0001172717,0.02600365,0.00004755503],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9519469,0.002090778,0.0001301961,0.01149049,0.0001375166,0.0001164007,0.002791545,0.00002777794,0.03126837],"genre_scores_gemma":[0.9891641,0.002543657,0.0003006608,0.001277803,0.00002598209,0.00004941413,0.0007862103,0.0000102184,0.005841937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9981217,"threshold_uncertainty_score":0.1178696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4808149758248864,"score_gpt":0.573191379401539,"score_spread":0.09237640357665255,"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."}}