{"id":"W4394819888","doi":"10.2196/52679","title":"Rolling the DICE (Design, Interpret, Compute, Estimate): Interactive Learning of Biostatistics With Simulations","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Karolinska Institutet; University of Pittsburgh","keywords":"Dice; Biostatistics; Computer science; Artificial intelligence; Machine learning; Statistics; Mathematics; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006952094,0.0001258787,0.0001789377,0.0001266042,0.0001032982,0.00006846366,0.0001789969,0.00007418749,0.000411643],"category_scores_gemma":[0.01033866,0.00007968018,0.0000318497,0.0003349049,0.0001811714,0.00008465539,0.00003704803,0.0004132083,0.00001518118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000674509,"about_ca_system_score_gemma":0.001124883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002418867,"about_ca_topic_score_gemma":0.000006865781,"domain_scores_codex":[0.9985948,0.0003612047,0.0003447787,0.0001921181,0.0003712735,0.0001358384],"domain_scores_gemma":[0.9875058,0.01187328,0.0001141045,0.0001781646,0.0002357067,0.0000929314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002422787,0.001378407,0.001124192,0.001552177,0.0004284546,0.00001321866,0.07554318,0.003390606,0.0003500314,0.5053468,0.1389968,0.2716338],"study_design_scores_gemma":[0.000404443,0.000396291,0.001511813,0.00295983,0.0002720218,0.00009087641,0.01150917,0.7518902,0.0009628153,0.2133093,0.01629748,0.0003957132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00946467,0.0001514697,0.9860251,0.001955853,0.001287354,0.0003945001,0.00001511955,0.0001250143,0.0005809055],"genre_scores_gemma":[0.7808799,0.00001609556,0.2181369,0.000172806,0.0001617192,0.00007635354,0.00004241777,0.0000244487,0.000489413],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7714152,"threshold_uncertainty_score":0.9979977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07512523238779197,"score_gpt":0.4814698182346228,"score_spread":0.4063445858468309,"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."}}