{"id":"W2286158066","doi":"10.1186/s40697-016-0099-4","title":"Utilizing Electronic Health Records to Predict Acute Kidney Injury Risk and Outcomes: Workgroup Statements from the 15 <sup>th</sup> ADQI Consensus Conference","year":2016,"lang":"en","type":"review","venue":"Canadian Journal of Kidney Health and Disease","topic":"Acute Kidney Injury Research","field":"Medicine","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Workgroup; Medicine; Acute kidney injury; Health records; Risk assessment; Intensive care medicine; Medical emergency; Family medicine; Internal medicine; Health care; Computer security; Law","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":[0.01893735,0.0007699358,0.001531102,0.004350843,0.0002688189,0.001949811,0.001285651,0.001528444,0.001204051],"category_scores_gemma":[0.01889614,0.0004223684,0.002200215,0.00405547,0.0005415987,0.002083203,0.0009527294,0.002157482,0.0006317198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179951,"about_ca_system_score_gemma":0.00876181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009552056,"about_ca_topic_score_gemma":0.01650793,"domain_scores_codex":[0.9976326,0.0009935749,0.0003705737,0.0001810031,0.0007407813,0.00008139155],"domain_scores_gemma":[0.9855281,0.009782978,0.0007031099,0.0002048302,0.003566879,0.0002140431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007686728,0.00004618055,0.001427779,0.02391645,0.0005724357,0.00005110473,0.0001492223,0.00039568,0.0001714385,0.003266375,0.02375704,0.9461694],"study_design_scores_gemma":[0.0001259,0.0003477438,0.01464799,0.1529865,0.004600925,0.0008239047,0.0006732114,0.001583622,0.00113518,0.007994968,0.8149147,0.0001653908],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"commentary","genre_scores_codex":[0.0002022225,0.9925652,0.0005759506,0.005661034,0.0003361469,0.00004982484,0.00007129963,0.000007447262,0.0005308801],"genre_scores_gemma":[0.001578532,0.9960207,0.001295807,0.000692097,0.000191738,0.00003969036,0.0000620692,0.000002060931,0.0001172999],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.01893735,"threshold_uncertainty_score":0.1001515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05309908645005088,"score_gpt":0.4015392173401513,"score_spread":0.3484401308901004,"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."}}