{"id":"W6907820501","doi":"10.25384/sage.21919607","title":"sj-xlsx-2-cjk-10.1177_20543581221149621 – Supplemental material for Population-Based Analysis of Nonsteroidal Anti-inflammatory Drug Prescription in Subjects With Chronic Kidney Disease","year":2023,"lang":"en","type":"dataset","venue":"Sage Journals Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kidney disease; Medical prescription; Drug; Nonsteroidal; Disease; Kidney; Chronic disease","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001369798,0.001351726,0.001609944,0.001787608,0.0009440366,0.002561459,0.002800365,0.002174268,0.229686],"category_scores_gemma":[0.00971642,0.001009554,0.001286254,0.003779417,0.0003191056,0.001002456,0.001502317,0.001785771,0.121552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001801351,"about_ca_system_score_gemma":0.002906019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04792962,"about_ca_topic_score_gemma":0.08112025,"domain_scores_codex":[0.9988993,0.0001611656,0.0001947647,0.0003313133,0.000223136,0.0001901427],"domain_scores_gemma":[0.9959954,0.001178752,0.0005983584,0.0007240465,0.000968106,0.000535293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002250694,0.00002545628,0.003223437,0.0005212526,0.00007115234,0.00002622436,0.0000161706,0.0001299837,0.00009731718,0.0002658116,0.9939573,0.00144088],"study_design_scores_gemma":[0.003766772,0.0001122276,0.05646092,0.001022845,0.0002999521,0.0002896772,0.000169001,0.0007103297,0.0008077111,0.00225988,0.9339964,0.0001042643],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001405843,0.00002353923,0.00003353652,0.00005282626,0.00001556267,0.00001224805,0.9992326,0.00007483506,0.0004141468],"genre_scores_gemma":[0.001084348,0.00003536287,0.0002182564,0.0001203349,0.00002187902,0.0001540363,0.9965615,0.00007322244,0.001731034],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.229686,"threshold_uncertainty_score":0.7683761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02287701738503526,"score_gpt":0.3021535406643129,"score_spread":0.2792765232792776,"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."}}