{"id":"W3198739944","doi":"10.1097/inf.0000000000003344","title":"Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis","year":2021,"lang":"en","type":"article","venue":"The Pediatric Infectious Disease Journal","topic":"Neonatal and Maternal Infections","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Hamilton Health Sciences; St. Joseph’s Healthcare Hamilton; McMaster Children's Hospital","funders":"Fonds NutsOhra; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Diagnostic accuracy; Population; Predictive modelling; Risk assessment; Clinical Practice; Sepsis; Neonatal sepsis","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":[0.008002131,0.001195743,0.001202098,0.002461181,0.0003281319,0.001011883,0.0007341327,0.0009687598,0.001108542],"category_scores_gemma":[0.02105864,0.0002162767,0.00123635,0.001010713,0.0003574955,0.0008810665,0.0005173373,0.001202683,0.000408004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008611103,"about_ca_system_score_gemma":0.001040334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002284452,"about_ca_topic_score_gemma":0.0009533114,"domain_scores_codex":[0.9977158,0.001212233,0.0002013107,0.0003829944,0.000347411,0.0001401574],"domain_scores_gemma":[0.9854363,0.01189442,0.0009205834,0.0004554062,0.001083136,0.0002101859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001515094,0.000986474,0.5555364,0.0002617463,0.00173944,0.0002470479,0.0001347651,0.2227762,0.00195626,0.0008331508,0.002433074,0.2115804],"study_design_scores_gemma":[0.00008597151,0.0007954965,0.03804084,0.00008516121,0.0002711393,0.0002263178,0.00004621294,0.9558895,0.001514452,0.002532858,0.0004824481,0.00002963427],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7685385,0.00226725,0.2241165,0.001042982,0.0002479742,0.0003479978,0.0009883057,0.0007566669,0.001693591],"genre_scores_gemma":[0.9692364,0.0002558624,0.02932639,0.0001166024,0.00007229923,0.0001726457,0.0005402326,0.00001919466,0.0002602984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008002131,"threshold_uncertainty_score":0.04231983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03587209789351224,"score_gpt":0.3173606691234518,"score_spread":0.2814885712299396,"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."}}