{"id":"W3126298132","doi":"10.1186/s12885-021-08928-9","title":"Nomogram based on autophagy related genes for predicting the survival in melanoma","year":2021,"lang":"en","type":"article","venue":"BMC Cancer","topic":"Autophagy in Disease and Therapy","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"SKiN Health","funders":"Xiangya Hospital, Central South University; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Nomogram; Receiver operating characteristic; Oncology; Univariate; Medicine; Lasso (programming language); Melanoma; Cohort; Concordance; Survival analysis; Internal medicine; Multivariate analysis; Area under the curve; Multivariate statistics; Cancer research; Computer science; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003430841,0.00104362,0.0008559598,0.003831805,0.0004946544,0.001232619,0.0005008188,0.0006197385,0.001259279],"category_scores_gemma":[0.006533543,0.0001298347,0.001195253,0.001280806,0.0004643288,0.0006021704,0.0007013569,0.0008574929,0.000469729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005653021,"about_ca_system_score_gemma":0.0007367515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001348329,"about_ca_topic_score_gemma":0.001702374,"domain_scores_codex":[0.9992037,0.0003052563,0.00008045183,0.0001646365,0.0001698175,0.0000761269],"domain_scores_gemma":[0.9966835,0.001600196,0.0005886281,0.0001800346,0.0006172277,0.0003304339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001285228,0.0001518718,0.8310698,0.0002429505,0.0004649794,0.0004791878,0.0001727706,0.05803077,0.002250797,0.0009147436,0.007974153,0.09696272],"study_design_scores_gemma":[0.0001941736,0.000811143,0.2646001,0.0002647218,0.0009171643,0.001540954,0.0005184737,0.7092392,0.003850059,0.006866788,0.01104824,0.0001489476],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8842967,0.004674668,0.09845402,0.001613412,0.0002942891,0.0003100745,0.006126144,0.0007271965,0.003503541],"genre_scores_gemma":[0.9744383,0.0004933014,0.02094817,0.0001328025,0.000116327,0.0001682606,0.003233937,0.00003846455,0.0004303467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003831805,"threshold_uncertainty_score":0.01814425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02936566652006624,"score_gpt":0.3198295605406832,"score_spread":0.290463894020617,"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."}}