{"id":"W4212961232","doi":"10.2196/33214","title":"Peer Review of “Selection of the Optimal L-asparaginase II Against Acute Lymphoblastic Leukemia: An In Silico Approach”","year":2021,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Acute Lymphoblastic Leukemia research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"In silico; Lymphoblastic Leukemia; Selection (genetic algorithm); Asparaginase; Computational biology; Leukemia; Medicine; Cancer research; Computer science; Biology; Internal medicine; Genetics; Machine learning; Gene","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02738026,0.0008145079,0.001663497,0.001586894,0.001685616,0.002941289,0.001846547,0.002439825,0.07984951],"category_scores_gemma":[0.1366677,0.0004475355,0.001745264,0.001144295,0.0008928959,0.001515098,0.001651705,0.001587371,0.02612059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001130904,"about_ca_system_score_gemma":0.01130297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001847041,"about_ca_topic_score_gemma":0.002986795,"domain_scores_codex":[0.985445,0.008173794,0.001006315,0.0008504638,0.004015825,0.0005086588],"domain_scores_gemma":[0.8828444,0.03051588,0.003813102,0.005769226,0.07355061,0.003506731],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001168274,0.0001316741,0.002091974,0.004320412,0.000372546,0.0003344437,0.0002241288,0.001506337,0.002973316,0.002413586,0.839554,0.1449095],"study_design_scores_gemma":[0.001135547,0.00062702,0.00411885,0.001759331,0.0007057323,0.0003478608,0.000400361,0.01076744,0.008248557,0.007129428,0.9646315,0.0001284057],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.06865759,0.01772789,0.1257181,0.2710078,0.2429096,0.02409452,0.04705231,0.01214095,0.1906912],"genre_scores_gemma":[0.3649789,0.01836452,0.2181466,0.04723261,0.05487911,0.009404712,0.06138131,0.005151846,0.2204604],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9726197,"threshold_uncertainty_score":0.2671232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139808500170919,"score_gpt":0.3075405659305899,"score_spread":0.2861424809288807,"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."}}