{"id":"W4405310893","doi":"10.1016/j.jstrokecerebrovasdis.2024.108198","title":"Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study","year":2024,"lang":"en","type":"article","venue":"Journal of Stroke and Cerebrovascular Diseases","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pupillometry; Ischemic stroke; Medicine; Stroke (engine); Computer science; Psychology; Neuroscience; Pupil; Cardiology; Engineering; Ischemia; Aerospace engineering","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":[0.001441366,0.001079516,0.001269975,0.0003745736,0.0007169918,0.0008211147,0.0004371644,0.0009860087,0.002922834],"category_scores_gemma":[0.003200435,0.0004107935,0.0008061467,0.0003186885,0.0008012784,0.00135021,0.0005988491,0.001407468,0.0009500884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004905662,"about_ca_system_score_gemma":0.0007675546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002123985,"about_ca_topic_score_gemma":0.00209782,"domain_scores_codex":[0.9993479,0.0002182547,0.00004091361,0.0002014241,0.00009231813,0.00009928386],"domain_scores_gemma":[0.9980487,0.0006898353,0.000204857,0.0002657795,0.0004309557,0.0003599674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.1045287,0.09339203,0.6592131,0.0006690468,0.001550017,0.001891843,0.002526792,0.000883402,0.02687188,0.0003308363,0.003160915,0.1049815],"study_design_scores_gemma":[0.01081435,0.1756007,0.7999218,0.00005476404,0.001031392,0.001714054,0.001392974,0.003295444,0.003549273,0.0004866053,0.002035289,0.0001034778],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986523,0.0001219128,0.0002616328,0.00006278006,0.00003105498,0.0003136977,0.0001205602,0.000008393688,0.0004277358],"genre_scores_gemma":[0.9975299,0.0001691472,0.0006817951,0.0001387667,0.0001004486,0.0004483128,0.0003375459,0.000007149064,0.0005868401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002922834,"threshold_uncertainty_score":0.009777844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008649213571146544,"score_gpt":0.2222163645569128,"score_spread":0.2135671509857663,"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."}}