{"id":"W2902335244","doi":"10.1371/journal.pone.0206410","title":"Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Complementary and Integrative Health; Fogarty International Center; National Institutes of Health; Grand Challenges Canada; Pontificia Universidad Católica del Perú; Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica","keywords":"Pneumonia; Lung ultrasound; Medicine; Ultrasound; Pattern recognition (psychology); Artificial intelligence; Lung; Computer science; Pathology; Radiology; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000467267,0.0003341913,0.0003018743,0.00175323,0.0001461229,0.0004340647,0.0003034666,0.000358259,0.0009305444],"category_scores_gemma":[0.001684887,0.0001376465,0.0003021928,0.0006244792,0.000204513,0.0003452654,0.000212481,0.0002067181,0.0003060776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003199333,"about_ca_system_score_gemma":0.0003226367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003631563,"about_ca_topic_score_gemma":0.003175101,"domain_scores_codex":[0.9996551,0.0000843837,0.00003391505,0.00008900226,0.00008897085,0.00004849326],"domain_scores_gemma":[0.99933,0.0002660505,0.0001028444,0.00003472608,0.0002396324,0.00002670407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004627039,0.0001363205,0.1015049,0.0001542288,0.00007634406,0.0005114943,0.0002653136,0.03306007,0.09515011,0.0008393359,0.001690271,0.7661489],"study_design_scores_gemma":[0.00002370668,0.0002600186,0.1852845,0.00003717248,0.00006587895,0.0006169409,0.0002749228,0.7729272,0.03744601,0.0009509056,0.002076483,0.00003630722],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7056603,0.0004093845,0.2889,0.0001533571,0.00004788159,0.0001987577,0.0004114168,0.001617865,0.002601066],"genre_scores_gemma":[0.8882425,0.0001846701,0.1096299,0.00003404722,0.00001734872,0.00009868742,0.0005739736,0.00003443792,0.001184397],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003631563,"threshold_uncertainty_score":0.007220864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028674889328362,"score_gpt":0.3190638709727131,"score_spread":0.2161963820398768,"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."}}