{"id":"W3110570880","doi":"10.1002/jor.24930","title":"Texture analysis in the classification of T<sub>2</sub>‐weighted magnetic resonance images in persons with and without low back pain","year":2020,"lang":"en","type":"article","venue":"Journal of Orthopaedic Research®","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Physiotherapy Association","keywords":"Magnetic resonance imaging; Medicine; Low back pain; Lumbar; Contrast (vision); Radiology; Nuclear medicine; Artificial intelligence; Computer science; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003460995,0.0001061311,0.0003591005,0.0006185984,0.00003177125,0.00004472576,0.0002420911,0.00005656855,0.00003177514],"category_scores_gemma":[0.0005863231,0.00006476223,0.0001029863,0.002819869,0.0002263928,0.000121735,0.00001830769,0.0009179648,0.000002077976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003332008,"about_ca_system_score_gemma":0.00006855509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000167885,"about_ca_topic_score_gemma":0.00006857572,"domain_scores_codex":[0.9976668,0.0006292359,0.0004536174,0.0001463166,0.0008600202,0.0002439527],"domain_scores_gemma":[0.9990796,0.0003512469,0.00009600411,0.0001597856,0.0001553573,0.0001580403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001318382,0.000117167,0.843016,0.0004365232,0.000217858,0.0002414908,0.003658516,0.001766461,0.03427046,0.00001400681,0.004800799,0.1113289],"study_design_scores_gemma":[0.001475113,0.0003478088,0.6263779,0.000582203,0.0001875236,0.00002505881,0.003473159,0.3654839,0.0009585327,0.00005352394,0.0008625611,0.0001727709],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865849,0.005637928,0.002878585,0.004527971,0.000008146413,0.0001060155,0.000005153924,0.000006567911,0.0002447372],"genre_scores_gemma":[0.9978746,0.001518796,0.000424236,0.00008120073,0.00006779402,0.000004048445,0.000002502803,0.00001219489,0.00001461292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3637174,"threshold_uncertainty_score":0.3988151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02336552634289717,"score_gpt":0.2730520586248023,"score_spread":0.2496865322819051,"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."}}