{"id":"W4390706506","doi":"10.1177/19714009231224428","title":"Diagnostic evaluation of deep learning accelerated lumbar spine MRI","year":2024,"lang":"en","type":"article","venue":"The Neuroradiology Journal","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Siemens Medical Solutions USA","keywords":"Medicine; Radiology; Spinal stenosis; Image quality; Magnetic resonance imaging; Lumbar; Nuclear medicine; Artificial intelligence; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.008242702,0.0003897628,0.0002906845,0.001148427,0.0001347388,0.0006193246,0.0004909874,0.0005100855,0.001007048],"category_scores_gemma":[0.02444488,0.0002224019,0.0002648945,0.0003367667,0.0004584769,0.0005181602,0.0006950549,0.0002913734,0.0002110848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004018787,"about_ca_system_score_gemma":0.0003097703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004129008,"about_ca_topic_score_gemma":0.0006018534,"domain_scores_codex":[0.9968306,0.001567245,0.0003631034,0.0003248369,0.0007944629,0.0001196356],"domain_scores_gemma":[0.9815662,0.008003801,0.003843838,0.001140984,0.004954753,0.0004904672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006854828,0.0005087458,0.7205498,0.0008539527,0.0005243228,0.001200779,0.0009881888,0.005787116,0.07626902,0.0002867345,0.0005152749,0.1856612],"study_design_scores_gemma":[0.0003326375,0.01101184,0.8440024,0.0002332975,0.0006743278,0.01111807,0.0006512009,0.06117504,0.06743572,0.0009858492,0.002255093,0.0001245664],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875571,0.0007575762,0.01047001,0.00006168656,0.00001746613,0.00009316018,0.00008048621,0.00006705138,0.0008955834],"genre_scores_gemma":[0.9906819,0.0001285951,0.008940142,0.00002842248,0.00001949362,0.00002014207,0.00008053148,0.000009052097,0.0000916015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008242702,"threshold_uncertainty_score":0.0435921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02373851503842874,"score_gpt":0.2840660537877635,"score_spread":0.2603275387493348,"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."}}