{"id":"W4415788087","doi":"10.1038/s41598-025-22164-2","title":"Simulation-based inference for subject-specific tuning of middle ear finite-element models towards personalized objective diagnosis","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Ear Surgery and Otitis Media","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute on Deafness and Other Communication Disorders; Canadian Institutes of Health Research; National Institutes of Health; Bundesministerium für Bildung und Forschung; International Max Planck Research School for Advanced Methods in Process and Systems Engineering; Deutsche Forschungsgemeinschaft; International Max Planck Research School for Environmental, Cellular and Molecular Microbiology","keywords":"Inference; Sensitivity (control systems); Artificial neural network; Set (abstract data type); Noise (video); Estimation theory; Posterior probability; Prior probability; Probability distribution","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":[0.003200694,0.0008013289,0.0007032311,0.0008757267,0.0002974147,0.0007726608,0.0009025443,0.0008948919,0.001264239],"category_scores_gemma":[0.01483536,0.0007259847,0.0007708535,0.00029852,0.0006599485,0.0005887078,0.000933949,0.001360647,0.0002576669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006414546,"about_ca_system_score_gemma":0.001150738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003990483,"about_ca_topic_score_gemma":0.005266467,"domain_scores_codex":[0.9992564,0.000366257,0.00004663978,0.0001543134,0.0001385167,0.00003773108],"domain_scores_gemma":[0.9954665,0.00360649,0.0002432907,0.0003000998,0.000319165,0.00006439287],"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":[0.00009062158,0.00004824143,0.003550461,0.0000790004,0.00005545834,0.00006017121,0.00008147503,0.9685113,0.004218918,0.002783596,0.0004525085,0.02006812],"study_design_scores_gemma":[0.000008622561,0.00001266586,0.0003805913,0.0000119163,0.000007412908,0.00001322766,0.000006975568,0.9954933,0.001158511,0.00264879,0.0002520581,0.000005996822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06236162,0.0002205027,0.9350761,0.0002426106,0.00002797205,0.00007423101,0.0001867524,0.0006569059,0.001153273],"genre_scores_gemma":[0.7901527,0.0001777935,0.2079999,0.0001846092,0.00002304146,0.0002577119,0.0004462962,0.0001647275,0.0005931954],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003990483,"threshold_uncertainty_score":0.01692712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0641489335374301,"score_gpt":0.3170671653761845,"score_spread":0.2529182318387544,"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."}}