{"id":"W7135319274","doi":"","title":"Détection précoce des accidents vasculaires cérébraux de type ischémiques à partir des images tomodensitométriques.","year":2019,"lang":"fr","type":"dissertation","venue":"Dépôt Institutionnel de lUniversité de Tlemcen","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Arterial disease; Medical screening; Lower limb; Limiting","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":[0.0007226825,0.0008115713,0.0005573972,0.003778923,0.0002703585,0.001191278,0.0003007496,0.0008242484,0.002958789],"category_scores_gemma":[0.00205641,0.0004598458,0.0004502194,0.001337881,0.0003412301,0.000702403,0.0003379251,0.0004539717,0.00114153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003045401,"about_ca_system_score_gemma":0.0006261172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004814194,"about_ca_topic_score_gemma":0.008394532,"domain_scores_codex":[0.9996812,0.00006605648,0.00002690161,0.00006966596,0.0001110058,0.0000451964],"domain_scores_gemma":[0.9991787,0.0002638648,0.0001427203,0.00008759351,0.0002731849,0.00005388282],"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":[0.001505987,0.00008333245,0.1202907,0.001328183,0.0004808216,0.004315746,0.0006171079,0.002816516,0.4174799,0.00108472,0.0026064,0.4473907],"study_design_scores_gemma":[0.0000953798,0.0009201961,0.5440304,0.0004331334,0.001167823,0.05065673,0.00109671,0.02940524,0.3255049,0.003127791,0.04341505,0.0001466147],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6491511,0.02893271,0.3032936,0.0008766689,0.0002592247,0.0003670327,0.001880678,0.003099255,0.01213974],"genre_scores_gemma":[0.8309374,0.01313964,0.1447366,0.0002267032,0.0001795708,0.0001742664,0.001437031,0.0001852395,0.008983666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004814194,"threshold_uncertainty_score":0.009898067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360632592846314,"score_gpt":0.2593655057035137,"score_spread":0.2457591797750505,"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."}}