{"id":"W4311672989","doi":"10.1186/s12883-022-02938-1","title":"Informing the development of an outcome set and banks of items to measure mobility among individuals with acquired brain injury using natural language processing","year":2022,"lang":"en","type":"article","venue":"BMC Neurology","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de réadaptation Lethbridge-Layton-Mackay; McGill University Health Centre; Centre Intégré de Santé et de Services Sociaux des Laurentides; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Mila - Quebec Artificial Intelligence Institute; Université de Montréal; Université Laval; Jewish Rehabilitation Hospital; McGill University; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Terminology; Computer science; Set (abstract data type); Natural language processing; Unified Medical Language System; Information retrieval; Artificial intelligence; Sentence; Cluster analysis; Ontology; Machine learning; Data mining; Linguistics","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":[0.04995697,0.001067244,0.001547848,0.01433396,0.001051218,0.004283182,0.001575427,0.001174385,0.005280049],"category_scores_gemma":[0.1490914,0.0006556379,0.003367646,0.008812429,0.001038716,0.005841292,0.002756334,0.001623542,0.001720687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003138451,"about_ca_system_score_gemma":0.01412074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006108247,"about_ca_topic_score_gemma":0.01139079,"domain_scores_codex":[0.9806723,0.009843227,0.005274941,0.00182692,0.002095149,0.0002873965],"domain_scores_gemma":[0.8291696,0.1243566,0.01391956,0.00741053,0.02404476,0.001098963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006329295,0.0004430041,0.1181425,0.0669555,0.001449024,0.000723532,0.01020307,0.004171937,0.01159873,0.009928349,0.03928167,0.7364697],"study_design_scores_gemma":[0.0009395484,0.002468838,0.4301651,0.08543792,0.00927953,0.002263528,0.03064953,0.0503392,0.03275586,0.05310791,0.3018469,0.0007462697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2669476,0.04000786,0.4994258,0.02323952,0.0008157504,0.02990407,0.1159874,0.003146066,0.02052593],"genre_scores_gemma":[0.2485692,0.007358882,0.6702077,0.001916525,0.000144007,0.02158116,0.04890469,0.0001952337,0.001122508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04995697,"threshold_uncertainty_score":0.2642008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09087254068227117,"score_gpt":0.3713350955770617,"score_spread":0.2804625548947905,"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."}}