{"id":"W2609113350","doi":"10.5539/ijsp.v6n3p204","title":"A New Margin Function for Anti-infective Trials","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Comisión de Operación y Fomento de Actividades Académicas, Instituto Politécnico Nacional; Consejo Nacional de Ciencia y Tecnología","keywords":"Margin (machine learning); Context (archaeology); Food and drug administration; Clinical trial; Medicine; Sample size determination; Selection (genetic algorithm); Drug trial; Statistics; Computer science; Medical physics; Mathematics; Machine learning; Risk analysis (engineering); Geography; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06366251,0.002242041,0.002847455,0.003035182,0.0007995988,0.00323786,0.003321514,0.00412569,0.003980483],"category_scores_gemma":[0.1269885,0.0009434786,0.002646519,0.00172755,0.003820491,0.008567872,0.003911872,0.005647535,0.001672997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002313202,"about_ca_system_score_gemma":0.002550533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005430075,"about_ca_topic_score_gemma":0.0003444829,"domain_scores_codex":[0.9626966,0.02929827,0.001464292,0.001932354,0.003958318,0.000650219],"domain_scores_gemma":[0.9063475,0.07546077,0.005195124,0.006459464,0.005241096,0.001296128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000823606,0.0001621114,0.003983842,0.0008718785,0.000302845,0.0003524161,0.00059572,0.1704608,0.003201793,0.5966283,0.009761751,0.212855],"study_design_scores_gemma":[0.0001183506,0.0005810407,0.001129832,0.0003017022,0.0001462737,0.0005003532,0.00007756498,0.6103098,0.001799224,0.3669712,0.01796956,0.00009513145],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002687965,0.001013154,0.9931237,0.0009184173,0.0001219663,0.0001501366,0.00007925667,0.0001795208,0.001725918],"genre_scores_gemma":[0.2396567,0.003251463,0.7419607,0.002697921,0.00113177,0.002796786,0.0005749615,0.0005829681,0.007346713],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06366251,"threshold_uncertainty_score":0.3366836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5299838594816128,"score_gpt":0.5780388171269906,"score_spread":0.04805495764537782,"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."}}