{"id":"W2946715901","doi":"10.1016/j.jmir.2019.03.151","title":"Implementing Technology to Drive Improvements within a High Volume Brachytherapy Program","year":2019,"lang":"en","type":"article","venue":"Journal of medical imaging and radiation sciences","topic":"Advances in Oncology and Radiotherapy","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Documentation; Workflow; Computer science; Process management; Brachytherapy; Software; Scheduling (production processes); Health care; Software engineering; Engineering management; Operations management; Operating system; Medicine; Engineering; Radiation therapy","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":[0.006211851,0.0003262062,0.0001429098,0.0005780248,0.001720285,0.002978017,0.001574623,0.001246094,0.005236481],"category_scores_gemma":[0.009883,0.0002523404,0.0003923674,0.0007294432,0.001067396,0.00233399,0.00255312,0.002592703,0.001145326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002810671,"about_ca_system_score_gemma":0.01843623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006788183,"about_ca_topic_score_gemma":0.009605495,"domain_scores_codex":[0.9963309,0.00118164,0.0001773584,0.0003664733,0.001354368,0.000589342],"domain_scores_gemma":[0.9929623,0.001398505,0.0008322148,0.0009741451,0.002469764,0.001362996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003822571,0.002546495,0.04331175,0.0004140622,0.0001063193,0.0004879892,0.003959043,0.01403463,0.04467073,0.04388767,0.05200884,0.7941902],"study_design_scores_gemma":[0.0004300445,0.005028561,0.09856901,0.0007635627,0.0003965874,0.001160145,0.007978918,0.04861762,0.149396,0.04150481,0.6458797,0.0002751769],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3707646,0.001596689,0.4073844,0.0938607,0.002082905,0.00220579,0.000671707,0.008210748,0.1132225],"genre_scores_gemma":[0.6958672,0.001020029,0.2787538,0.005990634,0.0002870645,0.0003447906,0.0004079628,0.0006811403,0.01664738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006788183,"threshold_uncertainty_score":0.03285176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007929814477856055,"score_gpt":0.4022368184742779,"score_spread":0.3943070039964219,"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."}}