{"id":"W4280606405","doi":"10.1177/10812865221098777","title":"Statistical prediction of bone microstructure degradation to study patient dependency in osteoporosis","year":2022,"lang":"en","type":"article","venue":"Mathematics and Mechanics of Solids","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Campus France","keywords":"Dependency (UML); Microstructure; Osteoporosis; Ground truth; Correlation coefficient; Materials science; Statistical model; Pearson product-moment correlation coefficient; Algorithm; Computer science; Statistics; Mathematics; Artificial intelligence; Medicine; Composite material","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005445514,0.00008966071,0.0003518482,0.0002101368,0.0000667112,0.000004726192,0.00004662093,0.0000416164,0.0001079027],"category_scores_gemma":[0.0001472387,0.00008226737,0.00002626408,0.0002508725,0.0000114856,0.00002216368,0.0001486288,0.0001909518,5.322642e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005712596,"about_ca_system_score_gemma":0.00008979323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009490831,"about_ca_topic_score_gemma":0.0000312828,"domain_scores_codex":[0.9984887,0.00004300615,0.0005874069,0.0001691882,0.000534878,0.0001767829],"domain_scores_gemma":[0.9993713,0.00006279117,0.0001338208,0.0001976881,0.0001051611,0.0001291806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","study_design_scores_codex":[0.003060891,0.02167129,0.04859782,0.01042933,0.0002935588,0.0002083194,0.106709,0.0002394663,0.6027474,0.05350836,0.001172968,0.1513616],"study_design_scores_gemma":[0.03555685,0.1107833,0.2102591,0.001578113,0.001242655,0.001143228,0.2906598,0.07818571,0.1665898,0.1014122,0.001007402,0.00158182],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965422,0.0001025021,0.00173858,0.0001365988,0.00005929499,0.001151786,0.0002242771,0.000007394647,0.00003732199],"genre_scores_gemma":[0.9867762,0.0000281868,0.01298149,0.00005621056,0.000006313663,0.00008486239,0.00003598958,0.00001413188,0.00001666898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4361576,"threshold_uncertainty_score":0.3354766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02158699311398762,"score_gpt":0.2974453787194983,"score_spread":0.2758583856055107,"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."}}