{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001675621,0.0002059984,0.000300812,0.0002610774,0.0001573031,0.00002166732,0.0002421404,0.00006524591,0.0000618956],"category_scores_gemma":[0.0001919013,0.0002007509,0.0002464165,0.0001317431,0.0003121569,0.0003085727,0.0001036579,0.0001698191,0.00002960619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000390061,"about_ca_system_score_gemma":0.00007606819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003828528,"about_ca_topic_score_gemma":0.00003090634,"domain_scores_codex":[0.9982909,0.0001263499,0.000535953,0.0004063999,0.000264718,0.0003756541],"domain_scores_gemma":[0.9989116,0.0003179427,0.0001757375,0.0003066757,0.00005110825,0.000237],"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":[0.06112054,0.004295296,0.04036361,0.001835271,0.0002354609,0.00003922682,0.04882202,0.0002156541,0.006302901,0.003364683,0.002122159,0.8312832],"study_design_scores_gemma":[0.00798436,0.001648208,0.8686852,0.001466066,0.0000886415,0.00001070696,0.00007608323,0.08792556,0.007582924,0.02018426,0.003730487,0.0006175232],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949426,0.0002343007,0.001178239,0.0008202989,0.0003431467,0.001139253,0.0006302473,0.00006736845,0.0006445555],"genre_scores_gemma":[0.9954081,0.00001331048,0.003278468,0.0003080831,0.000420451,0.00004168463,0.0004599063,0.00003223179,0.00003783321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8306656,"threshold_uncertainty_score":0.8186385,"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."}}