{"id":"W3127167953","doi":"10.3899/jrheum.201234","title":"Clinical Bedside Tools to Assess Systemic Sclerosis Vasculopathy: Can Digital Thermal Monitoring and Sublingual Microscopy Identify Patients With Digital Ulcers?","year":2021,"lang":"en","type":"article","venue":"The Journal of Rheumatology","topic":"Systemic Sclerosis and Related Diseases","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Research Resources; National Center for Advancing Translational Sciences; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institutes of Health; University of Utah; U.S. Department of Veterans Affairs","keywords":"Medicine; Internal medicine; Cardiology; Nuclear medicine; Gastroenterology; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001594715,0.000446702,0.0005642718,0.000971846,0.0002286281,0.0006105315,0.0002864933,0.0006134844,0.002245485],"category_scores_gemma":[0.005559257,0.0001901918,0.00029456,0.0006023562,0.0002981793,0.000680941,0.0005334291,0.0004263336,0.000496718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001229253,"about_ca_system_score_gemma":0.0001929639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000435489,"about_ca_topic_score_gemma":0.000856637,"domain_scores_codex":[0.9991703,0.0003976621,0.0000938177,0.0001125181,0.0001483996,0.00007723639],"domain_scores_gemma":[0.9974082,0.0008688097,0.0009133953,0.000142394,0.0002747484,0.0003924274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001451796,0.00002969054,0.993169,0.0000172945,0.00002660004,0.00005805003,0.00002534611,0.00004503148,0.0002986722,0.00001211438,0.0001027418,0.00607028],"study_design_scores_gemma":[0.00001975629,0.0004416955,0.9966739,0.00002788554,0.00004959126,0.0009446803,0.0001356617,0.001077503,0.0002368043,0.00004929439,0.0003360564,0.000007113087],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959941,0.00150664,0.0008423096,0.0001927131,0.00001740969,0.00003198927,0.0001980925,0.00001755838,0.00119913],"genre_scores_gemma":[0.9988141,0.0001775005,0.0006723955,0.00005067239,0.00003017461,0.00002371834,0.0001238615,0.000001334725,0.0001062846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002245485,"threshold_uncertainty_score":0.008433819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04444500230948367,"score_gpt":0.3139783383323406,"score_spread":0.269533336022857,"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."}}