{"id":"W2045714142","doi":"10.1038/sc.2008.149","title":"International Urinary Tract Imaging Basic Spinal Cord Injury Data Set","year":2008,"lang":"en","type":"article","venue":"Spinal Cord","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Hôpital Maisonneuve-Rosemont","funders":"Coloplast","keywords":"Medicine; Spinal cord injury; Data set; Data collection; Spinal cord; Computer science; Artificial intelligence; Psychiatry; 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":[0.001216842,0.001486314,0.002794064,0.007050934,0.0008061838,0.001499748,0.001873693,0.001504876,0.02433251],"category_scores_gemma":[0.008566126,0.0005315269,0.001632368,0.007475513,0.0004837114,0.0007984906,0.001174373,0.001242888,0.01413248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073097,"about_ca_system_score_gemma":0.004973097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02603528,"about_ca_topic_score_gemma":0.0248621,"domain_scores_codex":[0.9985865,0.000172239,0.000378413,0.0002469805,0.0003923129,0.0002234466],"domain_scores_gemma":[0.9954091,0.001324303,0.0006655075,0.0009064036,0.00123758,0.0004572451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.003852959,0.0007302164,0.06776123,0.003562489,0.001186282,0.0008978941,0.0001256014,0.005341738,0.001989696,0.001220176,0.8720831,0.04124856],"study_design_scores_gemma":[0.003381699,0.0008678546,0.2600661,0.001002575,0.00109874,0.003085243,0.0003736934,0.00806815,0.005586273,0.00228829,0.7138905,0.0002909731],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005144022,0.0001611428,0.0002081746,0.00007424456,0.00002845128,0.00009165235,0.9931839,0.0002067508,0.0009017751],"genre_scores_gemma":[0.008044803,0.0001052197,0.0003523073,0.00005878466,0.00001470055,0.0002362957,0.990487,0.00002713398,0.0006737498],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02603528,"threshold_uncertainty_score":0.08140028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2062748451986678,"score_gpt":0.4687473967701101,"score_spread":0.2624725515714423,"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."}}