{"id":"W4399628854","doi":"10.2196/58398","title":"Smartphone Pupillometry and Machine Learning for Detection of Acute Mild Traumatic Brain Injury: Cohort Study","year":2024,"lang":"en","type":"article","venue":"JMIR Neurotechnology","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Traumatic brain injury; Pupillometry; Glasgow Coma Scale; Medicine; Pupillary light reflex; Emergency department; Concussion; Cohort; Logistic regression; Physical therapy; Poison control; Pupil; Internal medicine; Injury prevention; Surgery; Psychology; Emergency medicine; Psychiatry","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":[0.00204395,0.0005310102,0.0006097523,0.0008754223,0.0008524274,0.0008898909,0.0005278169,0.0007756256,0.001858689],"category_scores_gemma":[0.004875355,0.000521191,0.001059658,0.000810448,0.0003848646,0.0009144808,0.0008592276,0.001076454,0.0004631134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003466169,"about_ca_system_score_gemma":0.0005887193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004514784,"about_ca_topic_score_gemma":0.004411068,"domain_scores_codex":[0.998816,0.0002698197,0.0001261185,0.0003893664,0.0002098074,0.0001888943],"domain_scores_gemma":[0.9978068,0.0003697089,0.0006112901,0.0004733634,0.0004058764,0.0003329345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007271274,0.0001406913,0.9968657,0.00001725564,0.000171375,0.0001022867,0.0001187167,0.00003136017,0.0002760349,0.00002393175,0.0001578713,0.001367654],"study_design_scores_gemma":[0.00004867995,0.0007695574,0.997366,0.00001151768,0.0001490739,0.0004155206,0.0002785655,0.0005076282,0.0001084234,0.00003810086,0.0002957711,0.00001111042],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991567,0.0001622935,0.0001969221,0.00002667408,0.000009423442,0.00003885189,0.0002524237,0.000003246657,0.0001533912],"genre_scores_gemma":[0.9990451,0.00009417848,0.0002236027,0.00002716433,0.00002049716,0.00006214058,0.0003356443,0.000005578291,0.0001860907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004514784,"threshold_uncertainty_score":0.01080954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06156150103683816,"score_gpt":0.3731163769355528,"score_spread":0.3115548758987146,"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."}}