{"id":"W2752998754","doi":"10.1007/978-3-319-66182-7_14","title":"Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study","year":2017,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre","funders":"National Center for Research Resources; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Pulmonary emphysema; COPD; Lung; Computer science; Texture (cosmology); Artificial intelligence; Spatial analysis; Pattern recognition (psychology); Medicine; Radiology; Image (mathematics); Internal medicine; Mathematics; Statistics","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":[0.0008741415,0.0003024787,0.0004997508,0.001125571,0.0002469257,0.0007882591,0.0004052146,0.0004620721,0.000572186],"category_scores_gemma":[0.002372604,0.0002287648,0.0006375624,0.00056127,0.0003841584,0.0003991937,0.0006288131,0.0004500783,0.0002357615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001352244,"about_ca_system_score_gemma":0.000303374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001766792,"about_ca_topic_score_gemma":0.005454216,"domain_scores_codex":[0.9997377,0.00008956214,0.00001604223,0.00007612415,0.00004968851,0.00003078843],"domain_scores_gemma":[0.9985952,0.000707415,0.0001676754,0.0002785255,0.0001398157,0.0001112094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004141478,0.001259302,0.5049602,0.0005075714,0.001274029,0.001141396,0.001039826,0.01874748,0.1192696,0.002642321,0.005400157,0.3396166],"study_design_scores_gemma":[0.0002035054,0.0007089656,0.7019591,0.00006837156,0.0005777377,0.003084397,0.001335617,0.2613222,0.02029964,0.004937947,0.005412191,0.00009035571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842256,0.0006708347,0.01372113,0.0001097907,0.00001291715,0.00002616747,0.0005891812,0.00008927201,0.0005550457],"genre_scores_gemma":[0.9871266,0.0002299928,0.01064631,0.00002566923,0.00003077762,0.00001340065,0.001185454,0.00003651445,0.0007052397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001766792,"threshold_uncertainty_score":0.004622996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02211743489887233,"score_gpt":0.3269417378348001,"score_spread":0.3048243029359278,"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."}}