{"id":"W2826532810","doi":"10.1136/archdischild-2018-314843","title":"Time interval for preterm infant weight gain velocity calculation precision","year":2018,"lang":"en","type":"article","venue":"Archives of Disease in Childhood Fetal & Neonatal","topic":"Infant Nutrition and Health","field":"Nursing","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; Alberta Health Services","funders":"","keywords":"Weight gain; Interval (graph theory); Noise (video); Growth velocity; Medicine; Intensive care; Body weight; Mathematics; Statistics; Computer science; Internal medicine; Intensive care medicine","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":[0.00533615,0.0009391516,0.0009195362,0.002113866,0.0005331585,0.00191444,0.001005074,0.0005605243,0.004995383],"category_scores_gemma":[0.03881126,0.0003328139,0.0009106094,0.001929942,0.0002738752,0.0007390804,0.0009020753,0.001135079,0.00212833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006755193,"about_ca_system_score_gemma":0.0009804622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005800464,"about_ca_topic_score_gemma":0.004228695,"domain_scores_codex":[0.9946175,0.001343868,0.0008434572,0.0006502007,0.002323325,0.0002216094],"domain_scores_gemma":[0.9859183,0.006456107,0.002290415,0.001163599,0.003805119,0.0003663965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.008988361,0.0003331525,0.2484104,0.002046984,0.0003904063,0.0009262853,0.001835968,0.005069208,0.01369097,0.00554548,0.02238205,0.6903809],"study_design_scores_gemma":[0.0004850449,0.005231007,0.7761824,0.002940467,0.001070532,0.005468905,0.001144342,0.03635392,0.06934714,0.004835795,0.09653047,0.0004100033],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4617445,0.02384593,0.4022467,0.001682956,0.005785505,0.002812562,0.01618676,0.00666353,0.07903175],"genre_scores_gemma":[0.8367286,0.004181419,0.1407913,0.0004317124,0.0003879519,0.003340936,0.005176038,0.001110152,0.007851874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005800464,"threshold_uncertainty_score":0.02822059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00889628918395028,"score_gpt":0.2759120164053214,"score_spread":0.2670157272213711,"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."}}