{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002081526,0.0002196969,0.0003480969,0.0003744753,0.0009439497,0.00002544019,0.0001146949,0.0002699668,0.000003840732],"category_scores_gemma":[0.0002939808,0.0002262034,0.00002423699,0.0005297706,0.00003794616,0.0001778966,0.000367363,0.0007973965,0.000002993449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008553702,"about_ca_system_score_gemma":0.0001761828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003228599,"about_ca_topic_score_gemma":0.00002978881,"domain_scores_codex":[0.9971777,0.0004904841,0.0007209331,0.0005625111,0.0004674398,0.0005808971],"domain_scores_gemma":[0.9985771,0.0006730309,0.0002535689,0.0001829928,0.0000868452,0.0002264879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001273876,0.00009565683,0.9553459,0.001428646,0.00001808245,0.00001852141,0.004812398,0.000931893,0.000001666553,0.00001311602,0.03128668,0.005920015],"study_design_scores_gemma":[0.006819595,0.0008498977,0.7310882,0.0002703842,0.0001465657,0.000008152666,0.0205481,0.1360368,0.00001363961,0.0003704356,0.1030561,0.000792071],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856043,0.001647386,0.008442133,0.0002470516,0.0006801376,0.002621832,0.0004751307,0.0002129877,0.00006897402],"genre_scores_gemma":[0.986358,0.0004910947,0.009595281,0.0002140041,0.0002002197,0.001231845,0.001151167,0.0000847374,0.0006736559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2242577,"threshold_uncertainty_score":0.9224306,"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."}}