{"id":"W4361297527","doi":"10.1515/cclm-2023-0209","title":"Detection of antinuclear antibodies: recommendations from EFLM, EASI and ICAP","year":2023,"lang":"en","type":"article","venue":"Clinical Chemistry and Laboratory Medicine (CCLM)","topic":"Systemic Lupus Erythematosus Research","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Cliniques Universitaires Saint-Luc; Assistance publique-Hôpitaux de Paris; Universität Innsbruck; Turun Yliopistollinen Keskussairaala; Medizinische Universität Innsbruck; Tartu Ülikool","keywords":"Anti-nuclear antibody; Medicine; Medical physics; Quality assurance; Medical laboratory; Delphi method; External quality assessment; Pathology; Immunology; Antibody; Computer science; Autoantibody; Artificial intelligence","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":[0.001455543,0.0001796664,0.0007241617,0.00006149784,0.0001021,0.000009443877,0.00009921766,0.0002880957,0.0003203757],"category_scores_gemma":[0.003937809,0.0001439697,0.00006161316,0.0005506978,0.0007782381,0.00006364312,0.0001101878,0.0005016926,0.00002728367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002107118,"about_ca_system_score_gemma":0.0001149092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002822251,"about_ca_topic_score_gemma":0.000002753231,"domain_scores_codex":[0.9979311,0.0001361064,0.0008765936,0.0004735651,0.0003319323,0.0002507045],"domain_scores_gemma":[0.9973406,0.001319195,0.0002259284,0.0004331129,0.0003009093,0.0003802783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005129761,0.0002176239,0.3952487,0.002705474,0.0003219351,0.00009205405,0.001080735,1.792758e-7,0.5669218,0.00001999126,0.0220842,0.01079444],"study_design_scores_gemma":[0.03467626,0.003277902,0.3841587,0.01608664,0.001587675,0.00113441,0.03989349,0.002495543,0.2439654,0.0011036,0.2700652,0.001555138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936943,0.001369131,0.00001949305,0.002946985,0.0002438482,0.0002208073,0.00006650664,0.0001228794,0.001316072],"genre_scores_gemma":[0.9937174,0.004136406,0.0001187454,0.000244751,0.0008576772,0.00001104613,0.00009262349,0.00003153226,0.0007898727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3229563,"threshold_uncertainty_score":0.5870913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05928375984773814,"score_gpt":0.3924603432218667,"score_spread":0.3331765833741285,"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."}}