{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004924071,0.0003838016,0.001089095,0.0002758254,0.0001906606,0.000030961,0.0003589879,0.0006339841,0.01385115],"category_scores_gemma":[0.0001733913,0.0003266681,0.0004641819,0.0003576863,0.0001200622,0.0001400144,0.000176296,0.001324685,0.00001833588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001865809,"about_ca_system_score_gemma":0.0004292049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002372369,"about_ca_topic_score_gemma":0.000009193968,"domain_scores_codex":[0.9970277,0.0004474986,0.0006591601,0.0007715364,0.0006223176,0.0004717986],"domain_scores_gemma":[0.9978249,0.0005198619,0.000632847,0.0004064565,0.0003865396,0.0002294119],"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":[0.01052124,0.00160202,0.1925746,0.006049862,0.0006081929,0.0003530711,0.0005350915,0.00007381874,0.01003145,0.0009496782,0.001122758,0.7755782],"study_design_scores_gemma":[0.03259083,0.02918201,0.6142811,0.009071112,0.003104768,0.00005018076,0.02042299,0.02577681,0.003314481,0.0001040463,0.2598667,0.002235047],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.7127938,0.0002879178,0.001105763,0.0001794282,0.001057334,0.005013005,0.00008488647,0.0001941521,0.2792838],"genre_scores_gemma":[0.4136953,0.0001731588,0.004996883,0.00004476742,0.0001275003,0.00001443659,0.001798788,0.0001006814,0.5790485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7733431,"threshold_uncertainty_score":0.9999185,"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."}}