{"id":"W4402217170","doi":"10.1016/j.is.2024.102459","title":"Tri-AL: An open source platform for visualization and analysis of clinical trials","year":2024,"lang":"en","type":"article","venue":"Information Systems","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Sleep Foundation; National Science Foundation","keywords":"Computer science; Visualization; Open source; Data science; Human–computer interaction; Information retrieval; Data mining; Operating system; Software","routes":{"ca_aff":true,"ca_fund":true,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01400076,0.00008563315,0.0005996565,0.000515464,0.00008118449,0.001100784,0.0005060013,0.00008558687,0.000004207984],"category_scores_gemma":[0.001422008,0.00006858969,0.0001070316,0.0009329992,0.0000151253,0.003590363,0.000136385,0.00008789468,0.000007418374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000243334,"about_ca_system_score_gemma":0.0001266569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003596979,"about_ca_topic_score_gemma":0.00001090199,"domain_scores_codex":[0.9969683,0.0004538487,0.002011389,0.0001763696,0.0002762865,0.0001138452],"domain_scores_gemma":[0.9971631,0.001457382,0.0007212147,0.0003428818,0.0002221877,0.00009326995],"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":[0.00008018966,0.00003036593,0.008475353,0.00131348,0.0006487229,4.679756e-7,0.01373571,0.01648563,0.000005988392,0.442407,0.002941354,0.5138757],"study_design_scores_gemma":[0.0002782668,0.0001482779,0.001510813,0.00006003161,0.00006687336,0.000002900694,0.0002763794,0.8827636,0.000004785188,0.00005758058,0.1147614,0.00006908036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0200802,0.0001307374,0.9770061,0.000204822,0.0008399819,0.00106664,0.00004153356,0.000171309,0.0004587298],"genre_scores_gemma":[0.9968504,0.00001720573,0.002357018,0.0002780734,0.0000854357,0.00009446099,0.0002136166,0.000006915686,0.00009683281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9767702,"threshold_uncertainty_score":0.9999362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2393030644157725,"score_gpt":0.5279758031200584,"score_spread":0.2886727387042859,"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."}}