{"id":"W4383059059","doi":"10.1002/nbm.4992","title":"Bringing MRI to low‐ and middle‐income countries: Directions, challenges and potential solutions","year":2023,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"St Joseph's Health Care; St Joseph's Health Centre; London Health Sciences Centre; Western University","funders":"","keywords":"Magnetic resonance imaging; Low and middle income countries; Teleradiology; Quality (philosophy); Computer science; Business; Developing country; Medicine; Health care; Economic growth; Telemedicine; Radiology; Economics","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":[0.0002472946,0.0001113529,0.0002180632,0.0004539638,0.0001192505,0.000007315284,0.00004099218,0.00006811335,0.00001871274],"category_scores_gemma":[0.00005115596,0.00009989242,0.00001730127,0.0005352729,0.0001392666,0.00004698228,0.00008859987,0.0001190463,0.00001761289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005101814,"about_ca_system_score_gemma":0.00001912075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004367193,"about_ca_topic_score_gemma":0.000022483,"domain_scores_codex":[0.9990807,0.000008938076,0.0002114002,0.0002803914,0.0001491579,0.0002694748],"domain_scores_gemma":[0.9995081,0.00005829414,0.00003153458,0.0001915976,0.000045891,0.0001646194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0013181,0.00185294,0.02989362,0.01097713,0.0004362686,0.002484029,0.02595552,0.0005672051,0.1177624,0.09476694,0.03094918,0.6830367],"study_design_scores_gemma":[0.005761051,0.001349493,0.5537166,0.00847814,0.0002358835,0.001422248,0.00531307,0.007886224,0.001996412,0.006638847,0.4062764,0.0009256108],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7183697,0.03286127,0.03126011,0.2056777,0.0004745516,0.003136996,0.0001024375,0.001961989,0.006155234],"genre_scores_gemma":[0.9474502,0.04298655,0.007360978,0.0004706491,0.0003138234,0.0001984297,0.00003372153,0.00003063854,0.001155057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.682111,"threshold_uncertainty_score":0.4073495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02854450070144405,"score_gpt":0.314848963799405,"score_spread":0.286304463097961,"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."}}