{"id":"W7028985188","doi":"","title":"Improved sequential decision-making with structural priors: Enhanced treatment personalization with historical data","year":2023,"lang":"en","type":"other","venue":"Chalmers Research (Chalmers University of Technology)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Vetenskapsrådet; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; Knut och Alice Wallenbergs Stiftelse; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Prior probability; Benchmark (surveying); Estimator; Benchmarking; Matching (statistics); Latent variable; Set (abstract data type); Bayesian probability","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.009646167,0.0008127642,0.001522056,0.0006369192,0.0004541401,0.001449595,0.001946173,0.001629649,0.004801468],"category_scores_gemma":[0.03324971,0.0008434452,0.0009894794,0.0007406446,0.00093043,0.002445549,0.00139286,0.002792515,0.0004485973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001439639,"about_ca_system_score_gemma":0.002857319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004658792,"about_ca_topic_score_gemma":0.004376737,"domain_scores_codex":[0.9958925,0.00270174,0.00016126,0.000606182,0.0003880763,0.000250236],"domain_scores_gemma":[0.9693657,0.02553304,0.001744807,0.002051412,0.0006765936,0.0006284554],"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":[0.000478696,0.0002086246,0.001410515,0.00007823513,0.00007028057,0.0000483994,0.0001330083,0.9346963,0.0005741368,0.01984748,0.0006659052,0.04178838],"study_design_scores_gemma":[0.0001045579,0.0001429942,0.000262066,0.00001839553,0.00001708406,0.00002071566,0.00001390773,0.9776865,0.0004370403,0.02080827,0.0004764122,0.00001203493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.102152,0.0004102198,0.8913475,0.0009908932,0.00005276507,0.0003551086,0.0002911835,0.0004808286,0.003919584],"genre_scores_gemma":[0.7958478,0.0003044482,0.2008418,0.0002866059,0.0000413121,0.0003813888,0.0004148798,0.00005820746,0.001823412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009646167,"threshold_uncertainty_score":0.05101436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07157517403928527,"score_gpt":0.3441162500382112,"score_spread":0.272541075998926,"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."}}