{"id":"W1972479177","doi":"10.1186/1756-0500-3-267","title":"Structural equation modeling in medical research: a primer","year":2010,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":481,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Structural equation modeling; Latent variable; Computer science; Medical research; Set (abstract data type); Data science; Linear regression; Measure (data warehouse); Management science; Data mining; Machine learning; Medicine; Pathology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02760125,0.001203256,0.001624469,0.004843024,0.001027058,0.004174825,0.002315684,0.005008689,0.009231743],"category_scores_gemma":[0.0292089,0.001135726,0.001492184,0.007032516,0.005509799,0.005466542,0.002602441,0.01082756,0.002315046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002778908,"about_ca_system_score_gemma":0.005782072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002090515,"about_ca_topic_score_gemma":0.001856874,"domain_scores_codex":[0.9853431,0.01187978,0.0007938854,0.0005507391,0.001260475,0.0001719785],"domain_scores_gemma":[0.9389548,0.05603892,0.001452771,0.001016638,0.002059545,0.0004773712],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002405304,0.00012396,0.001427828,0.002119042,0.00009850193,0.0003321781,0.002369715,0.005151961,0.0002935961,0.8377829,0.02280849,0.1274677],"study_design_scores_gemma":[0.00003093714,0.00009882329,0.0008851787,0.004777368,0.00004064067,0.0005164428,0.0006846864,0.007981343,0.0001928564,0.7824855,0.2022509,0.0000552546],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001679964,0.1160948,0.74726,0.1087066,0.002334059,0.0008233075,0.000838165,0.000334242,0.02192884],"genre_scores_gemma":[0.03935033,0.1709885,0.7633828,0.01429937,0.003915932,0.003680868,0.0008566345,0.0001848451,0.003340762],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9723988,"threshold_uncertainty_score":0.1459711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9628487103961001,"score_gpt":0.6876974379363604,"score_spread":0.2751512724597397,"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."}}