{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008164998,0.00099321,0.0008784906,0.001585958,0.00265764,0.008729124,0.003039024,0.01155853,0.01776898],"category_scores_gemma":[0.01067071,0.0004696511,0.001045706,0.001685749,0.004339149,0.01031963,0.007855023,0.008511983,0.005021884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003505458,"about_ca_system_score_gemma":0.02324408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006171597,"about_ca_topic_score_gemma":0.01037756,"domain_scores_codex":[0.9965444,0.001526607,0.0001898653,0.0003285228,0.0005689448,0.0008416618],"domain_scores_gemma":[0.9879095,0.004678491,0.0009402056,0.0002979802,0.002497764,0.003676086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001960758,0.0004960033,0.00435902,0.01202614,0.0001342792,0.002047086,0.003134748,0.001844256,0.002621929,0.1466994,0.2839169,0.5425241],"study_design_scores_gemma":[0.00006420621,0.0003632202,0.004191777,0.01231183,0.0001104545,0.002142996,0.02024982,0.002134769,0.001307665,0.1648618,0.7921004,0.0001611414],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002759572,0.1656666,0.007090531,0.8085396,0.004212191,0.00007582649,0.0001868464,0.0002084949,0.01126031],"genre_scores_gemma":[0.09374403,0.6573585,0.06202421,0.155007,0.01655281,0.0006607744,0.0009408284,0.0001595993,0.01355237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01776898,"threshold_uncertainty_score":0.05944318,"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."}}