{"id":"W3158234031","doi":"10.22215/etd/2016-11361","title":"Integration of Artifact Detection in Clinical Decision Support Systems","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Interoperability; Workflow; Reusability; Clinical decision support system; Artifact (error); Component (thermodynamics); Scalability; Flexibility (engineering); Decision support system; Data mining; Software engineering; Artificial intelligence; Database; Software","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007328948,0.0001333461,0.0004890942,0.0004404465,0.00002163953,0.000002896833,0.00005335738,0.001030613,0.00006175465],"category_scores_gemma":[0.0006863525,0.00009227878,0.0001023005,0.0001635039,0.00002014628,0.00005256263,0.000005726821,0.000509197,0.00006170828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001058517,"about_ca_system_score_gemma":0.0001818334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000269392,"about_ca_topic_score_gemma":0.0009748999,"domain_scores_codex":[0.9981666,0.00007083841,0.001145534,0.0002513399,0.0002235605,0.0001421],"domain_scores_gemma":[0.9989008,0.0002341016,0.0003314666,0.0002656357,0.0002073872,0.00006064768],"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":[0.0009010983,0.00009234217,0.05192628,0.0003006094,0.00002000651,0.00001173864,0.00009922162,1.614544e-7,0.007721543,0.00008821008,0.00005789107,0.9387809],"study_design_scores_gemma":[0.002559042,0.00347642,0.8034119,0.009218024,0.0001337977,0.00003413901,0.001978998,0.0002471113,0.176587,0.0003698633,0.001637912,0.0003457296],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989986,0.00008167326,0.00149919,0.00002741273,0.003610381,0.0005490801,0.000002641041,0.00008051463,0.00416313],"genre_scores_gemma":[0.9964101,0.0002163886,0.0002230211,0.000008727789,0.0001745743,0.00003473026,0.0001546525,0.00001739821,0.00276035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9384351,"threshold_uncertainty_score":0.7949027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06574146643624172,"score_gpt":0.4353678709512641,"score_spread":0.3696264045150224,"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."}}