{"id":"W4407116510","doi":"10.2196/preprints.71726","title":"Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial (Preprint)","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computed tomographic angiography; Coronary angiography; Preprint; Medicine; Randomized controlled trial; Protocol (science); Radiology; Computed tomographic; Angiography; Internal medicine; Computed tomography; Computer science; Pathology","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":[0.0094374,0.003599448,0.005496067,0.0009420777,0.001297133,0.002931068,0.001481083,0.003799995,0.04304682],"category_scores_gemma":[0.01317751,0.001482237,0.004404222,0.001246473,0.001603739,0.00190927,0.001117679,0.004431072,0.006030295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002452145,"about_ca_system_score_gemma":0.006025298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002497331,"about_ca_topic_score_gemma":0.004138963,"domain_scores_codex":[0.9940774,0.003585923,0.00053041,0.0006411137,0.0005033455,0.0006616922],"domain_scores_gemma":[0.996262,0.0009653026,0.0009289924,0.0004681293,0.0006927461,0.0006827958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.9597989,0.00392154,0.0005912771,0.005357416,0.002139508,0.00007444323,0.00009224169,0.000596831,0.0007817846,0.001213259,0.0152901,0.01014262],"study_design_scores_gemma":[0.9816378,0.008212095,0.00103609,0.0004918679,0.0008278078,0.00001978917,0.00002900644,0.0005596564,0.0002176145,0.0009058776,0.006031598,0.00003079621],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.01979114,0.001574484,0.002459662,0.001649657,0.001814004,0.9606449,0.007670451,0.0002737298,0.004121847],"genre_scores_gemma":[0.0206497,0.0004675215,0.003347028,0.001289334,0.0002770137,0.9712308,0.001153641,0.00001906946,0.001565851],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.04304682,"threshold_uncertainty_score":0.144006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02118940354270695,"score_gpt":0.3066319500069792,"score_spread":0.2854425464642723,"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."}}