{"id":"W4404234408","doi":"10.1055/s-0044-1791750","title":"Screening for atrial fibrillation after stroke: is targeted patient selection the key?","year":2024,"lang":"en","type":"article","venue":"Arquivos de Neuro-Psiquiatria","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Atrial fibrillation; Key (lock); Stroke (engine); Selection (genetic algorithm); Medicine; Cardiology; Internal medicine; Computer science; Computer security; Artificial intelligence; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02139217,0.001061184,0.006103098,0.001799738,0.001614329,0.006485424,0.003160353,0.008753014,0.01125576],"category_scores_gemma":[0.09317032,0.0007145372,0.002233759,0.001516627,0.002183995,0.009359294,0.00371924,0.01542418,0.002121041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002228848,"about_ca_system_score_gemma":0.01185373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006070383,"about_ca_topic_score_gemma":0.008142958,"domain_scores_codex":[0.9855824,0.0072056,0.002194542,0.001283614,0.002451774,0.001282069],"domain_scores_gemma":[0.9459133,0.0284874,0.005002795,0.001468956,0.009422942,0.009704596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001482726,0.001490443,0.08400736,0.004025805,0.001207177,0.00129142,0.001797348,0.001061754,0.0005537143,0.01230645,0.2696919,0.6210839],"study_design_scores_gemma":[0.004799277,0.004254463,0.3081398,0.1262416,0.005105774,0.00668205,0.01669497,0.006872365,0.0009077524,0.1779385,0.3410433,0.001320105],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.009395502,0.09751131,0.005434042,0.8637295,0.01183832,0.0002745707,0.0003514968,0.0001613399,0.01130384],"genre_scores_gemma":[0.2640117,0.2434941,0.01856903,0.3826602,0.08490174,0.001660745,0.001527726,0.0002182111,0.002956528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02139217,"threshold_uncertainty_score":0.113134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03607104618846064,"score_gpt":0.3082180295592835,"score_spread":0.2721469833708229,"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."}}