{"id":"W4388701400","doi":"10.3389/fped.2023.1264527","title":"Respiratory distress syndrome prediction at birth by optical skin maturity assessment and machine learning models for limited-resource settings: a development and validation study","year":2023,"lang":"en","type":"article","venue":"Frontiers in Pediatrics","topic":"Neonatal skin health care","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação Oswaldo Cruz; Grand Challenges Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Government of Canada; Bill and Melinda Gates Foundation","keywords":"Medicine; Respiratory distress; Resource (disambiguation); Maturity (psychological); Respiratory system; Intensive care medicine; Pediatrics; Developmental psychology; Internal medicine; Surgery; Computer science; Psychology","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":[0.01119027,0.001336881,0.0008452209,0.0008249945,0.0003333036,0.000700995,0.001317748,0.0008971157,0.0007649226],"category_scores_gemma":[0.01458414,0.0004109972,0.001108113,0.0005070007,0.0003972428,0.0006425817,0.0008787585,0.00110201,0.0003134276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008306935,"about_ca_system_score_gemma":0.001209526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01018618,"about_ca_topic_score_gemma":0.005065448,"domain_scores_codex":[0.9971993,0.00194044,0.0001598134,0.0003601513,0.0002207089,0.0001195373],"domain_scores_gemma":[0.9848753,0.0113545,0.0006873802,0.00107479,0.001679426,0.0003286403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003389235,0.005100704,0.547344,0.0003615823,0.001857566,0.0003693823,0.0004181189,0.2667032,0.003728754,0.0004045384,0.001952203,0.1683707],"study_design_scores_gemma":[0.0002234979,0.002257596,0.09428307,0.00007499592,0.0002394693,0.0001292215,0.0001491664,0.9003049,0.0017035,0.0002142607,0.000385776,0.00003448597],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884403,0.0004287945,0.00987505,0.00009525464,0.00002279319,0.0002104509,0.0003954541,0.0001032725,0.0004286623],"genre_scores_gemma":[0.9882961,0.0001781576,0.009919667,0.00003085881,0.00001123725,0.0001614062,0.001154679,0.00001176869,0.0002362743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01119027,"threshold_uncertainty_score":0.0591805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04215899105971526,"score_gpt":0.3415187564279074,"score_spread":0.2993597653681921,"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."}}