{"id":"W4240967627","doi":"10.18535/jmscr/v4i1.05","title":"High Resolution Computed Tomography in Interstitial Lung Diseases","year":2016,"lang":"en","type":"article","venue":"Journal of Medical Science And clinical Research","topic":"Medical Imaging and Pathology Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"ASTER","funders":"","keywords":"Medicine; Computed tomography; High-resolution computed tomography; Radiology; Resolution (logic); Tomography; Interstitial lung disease; High resolution; Nuclear medicine; Lung; Internal medicine; Artificial intelligence; Remote sensing; Geology","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":[0.000523049,0.0002179206,0.0002115547,0.000768862,0.0002234153,0.0003115646,0.0002044615,0.0003266764,0.001424362],"category_scores_gemma":[0.001474901,0.0001575264,0.0001587883,0.0005159417,0.0003157634,0.000315929,0.0002358141,0.000253716,0.0002529535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001007359,"about_ca_system_score_gemma":0.0001551847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006088797,"about_ca_topic_score_gemma":0.0008334594,"domain_scores_codex":[0.9997512,0.0001183473,0.00002569611,0.0000251435,0.00004310649,0.00003651673],"domain_scores_gemma":[0.9994138,0.0001977268,0.000194396,0.00003750812,0.00006395466,0.0000925402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004870068,0.00009234927,0.9820467,0.00007074522,0.00003008562,0.003148074,0.0001135985,0.00008710429,0.004224749,0.00005589932,0.0001062411,0.009537353],"study_design_scores_gemma":[0.00002038227,0.0004206126,0.9811562,0.00003038906,0.00005220998,0.01600879,0.0001888993,0.0002359294,0.00102737,0.00006420002,0.0007894121,0.000005678807],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928444,0.005174875,0.0003442776,0.00007194509,0.00001189362,0.00001397562,0.00005451504,0.000005247629,0.001478978],"genre_scores_gemma":[0.998582,0.0008750593,0.0002344642,0.0000292518,0.00002516949,0.000004082286,0.00005160308,9.108185e-7,0.0001975117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001424362,"threshold_uncertainty_score":0.004764974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.164902793410295,"score_gpt":0.5410084280575058,"score_spread":0.3761056346472108,"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."}}