{"id":"W2132533740","doi":"10.4321/s0212-71992006000600004","title":"Utilidad de los modelos clínicos en la predicción de tromboembolia pulmonar","year":2006,"lang":"es","type":"article","venue":"Anales de Medicina Interna","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Probability model; Pulmonary oedema; Pre- and post-test probability; Internal medicine; Cardiology; Lung; Statistics","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":[0.006514353,0.001874991,0.001003765,0.003357864,0.0005103087,0.00247339,0.0008954509,0.001033003,0.002123847],"category_scores_gemma":[0.03434514,0.0003779954,0.001569775,0.001366775,0.0006213014,0.0007466435,0.0008256874,0.0008886606,0.0005775284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001220079,"about_ca_system_score_gemma":0.002540431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03485742,"about_ca_topic_score_gemma":0.0198428,"domain_scores_codex":[0.9975079,0.001380374,0.0001527459,0.0003150227,0.0004486968,0.0001952452],"domain_scores_gemma":[0.981137,0.01396057,0.001761106,0.0004768589,0.002022073,0.0006423747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009833416,0.0002067707,0.9187759,0.0001529729,0.0005701444,0.0002163094,0.0001065255,0.04648004,0.0002159979,0.0003455791,0.002022717,0.02992359],"study_design_scores_gemma":[0.0001768064,0.0007923226,0.2468159,0.0002475356,0.0006831016,0.001081853,0.0003291705,0.744651,0.0004726904,0.002471079,0.002197844,0.00008061029],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9440666,0.003638701,0.03881663,0.001691509,0.0002355919,0.0005222558,0.003602783,0.0007869423,0.006639082],"genre_scores_gemma":[0.991174,0.0003072853,0.006826917,0.00006577578,0.00005357537,0.0001012288,0.001087291,0.00001836627,0.0003657126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03485742,"threshold_uncertainty_score":0.06930906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007508137393086512,"score_gpt":0.2835881128126428,"score_spread":0.2760799754195563,"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."}}