{"id":"W3076693518","doi":"10.1097/md.0000000000021828","title":"A bibliometric analysis of income and cardiovascular disease","year":2020,"lang":"en","type":"review","venue":"Medicine","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Science Citation Index; Index (typography); Bibliometrics; Disease; Environmental health; Public health; Obesity; Gerontology; Population; Citation; Pathology; Library science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01055944,0.001029368,0.003510424,0.1030829,0.0008888436,0.003086794,0.001265198,0.0007769716,0.004725816],"category_scores_gemma":[0.04442289,0.000416643,0.004434285,0.1253269,0.0006312263,0.002590704,0.00183298,0.0005884127,0.0005267165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002977509,"about_ca_system_score_gemma":0.007555368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006693219,"about_ca_topic_score_gemma":0.009134269,"domain_scores_codex":[0.9877893,0.003509414,0.003093439,0.0007506277,0.004512479,0.0003447885],"domain_scores_gemma":[0.9678717,0.02022661,0.004854022,0.0007392854,0.005927654,0.0003807804],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004312424,0.0002060202,0.1358823,0.2769046,0.01599559,0.0006941309,0.002336315,0.002154674,0.0008168735,0.006268137,0.02771382,0.5305964],"study_design_scores_gemma":[0.0002960521,0.0006601605,0.5962323,0.1199984,0.05290343,0.002434412,0.006460501,0.006909947,0.001784828,0.0097903,0.2021862,0.0003434417],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.09792233,0.8224533,0.006628836,0.00508337,0.0009139767,0.003461592,0.03813176,0.0003382554,0.02506662],"genre_scores_gemma":[0.445934,0.5108759,0.01716445,0.0005598155,0.001030488,0.004331701,0.01765535,0.00006329222,0.002384983],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9894406,"threshold_uncertainty_score":0.05584431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09230446196376621,"score_gpt":0.4231286600761334,"score_spread":0.3308241981123672,"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."}}