{"id":"W4388967905","doi":"10.1038/s41598-023-47935-7","title":"Accurate personalized survival prediction for amyotrophic lateral sclerosis patients","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Amyotrophic Lateral Sclerosis Research","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"Fondation Brain Canada","keywords":"Amyotrophic lateral sclerosis; Margin (machine learning); Proportional hazards model; Event (particle physics); Survival analysis; Medicine; Magnetic resonance imaging; Computer science; Artificial intelligence; Physical medicine and rehabilitation; Disease; Machine learning; Pathology; Internal medicine; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002108442,0.0005487012,0.000631127,0.0005488252,0.0002247245,0.0006536648,0.0004364546,0.0008444219,0.001399445],"category_scores_gemma":[0.009635199,0.0001705409,0.0004452182,0.0002740308,0.0002004212,0.0006801157,0.0005945091,0.001061613,0.0004769933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004723773,"about_ca_system_score_gemma":0.0007241081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002344261,"about_ca_topic_score_gemma":0.002347829,"domain_scores_codex":[0.9994079,0.000260194,0.00004183247,0.000149912,0.00008624467,0.00005395556],"domain_scores_gemma":[0.9959103,0.002969567,0.0003260929,0.0002520343,0.0003277172,0.0002143296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001109948,0.0002026069,0.1089105,0.00009885859,0.0001480828,0.0004133456,0.0001871739,0.6601463,0.00204191,0.001896687,0.01200262,0.2128419],"study_design_scores_gemma":[0.00002453083,0.0000806948,0.005253806,0.00002117681,0.0000244139,0.0001081417,0.00003396628,0.987901,0.001221893,0.004363889,0.0009496508,0.00001690112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6603137,0.001913592,0.3249269,0.004575348,0.0001807868,0.00008229422,0.002707517,0.002466505,0.00283324],"genre_scores_gemma":[0.9782147,0.0002142089,0.01935337,0.0002373243,0.00007665269,0.00002948808,0.001210229,0.00004664004,0.000617303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002344261,"threshold_uncertainty_score":0.01115066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0740230377592657,"score_gpt":0.3155553279734595,"score_spread":0.2415322902141938,"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."}}