{"id":"W2972702565","doi":"","title":"Stepping Beyond Behaviour: Explainable Machine Learning for Clinical Neurophysiological Assessment of Concussion Progression","year":2019,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; McMaster University; Ontario Centres of Excellence","keywords":"Concussion; Neurophysiology; Physical medicine and rehabilitation; Psychology; Medicine; Neuroscience; Computer science; Cognitive science; Machine learning; Artificial intelligence; Medical emergency; Injury prevention; Poison control","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001634334,0.0005626056,0.0003697408,0.0007809495,0.0002557664,0.001157671,0.0004868056,0.0005690471,0.00109127],"category_scores_gemma":[0.006041357,0.0001962979,0.0005036046,0.0004919122,0.0004022426,0.0006036153,0.0004915098,0.001130472,0.0002151549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00066513,"about_ca_system_score_gemma":0.0006685166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002644551,"about_ca_topic_score_gemma":0.003953531,"domain_scores_codex":[0.9997013,0.000145839,0.00001996203,0.00007642464,0.00003610578,0.00002028337],"domain_scores_gemma":[0.9983228,0.001337798,0.0001288985,0.00009887697,0.00008218543,0.00002937155],"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":[0.0001661749,0.0003636964,0.04280043,0.0003136052,0.0003112255,0.000206811,0.0009323552,0.2361717,0.005174856,0.02309859,0.00253095,0.6879296],"study_design_scores_gemma":[0.00001101652,0.0001336846,0.02159864,0.0001051573,0.00005196777,0.00007486485,0.0001516708,0.9299577,0.000965429,0.04490659,0.002012264,0.00003107914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2345723,0.005785526,0.7500027,0.003754912,0.0001407672,0.0003045839,0.0007044107,0.000732973,0.004001856],"genre_scores_gemma":[0.829388,0.002179573,0.1651802,0.0002017533,0.0001224912,0.0002867836,0.0005063658,0.0000420369,0.002092688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002644551,"threshold_uncertainty_score":0.00864327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07359577548876259,"score_gpt":0.3823888804804675,"score_spread":0.308793104991705,"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."}}