{"id":"W4280545699","doi":"10.3389/fneur.2022.884693","title":"Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neurology","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Receiver operating characteristic; Stroke (engine); Modified Rankin Scale; Interpretability; Neuroimaging; Radiology; Computed tomography angiography; Angiography; Machine learning; Ischemic stroke; Internal medicine; Ischemia; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003637438,0.001132359,0.0005922653,0.001173874,0.0002805652,0.001017217,0.0008172269,0.0008504291,0.001075628],"category_scores_gemma":[0.01179773,0.000319046,0.0009354772,0.0004939729,0.0005985292,0.0006664037,0.0006477612,0.001174921,0.0002918722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007421241,"about_ca_system_score_gemma":0.0006340046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003329085,"about_ca_topic_score_gemma":0.001991648,"domain_scores_codex":[0.99886,0.0007622848,0.00006373879,0.0001368824,0.0001296251,0.00004746415],"domain_scores_gemma":[0.9932161,0.005601653,0.0005215658,0.000214268,0.0003823006,0.00006407292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009626069,0.00008674776,0.01466828,0.00005627874,0.0001663346,0.000104739,0.00009329905,0.9529269,0.0006219258,0.002814293,0.0004676398,0.02789733],"study_design_scores_gemma":[0.000003200358,0.00002004557,0.00059033,0.000005526538,0.000009087188,0.00001105504,0.000004228051,0.9958985,0.0001102202,0.003264455,0.0000799451,0.00000336566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1693821,0.0007807637,0.826381,0.001061914,0.00004878833,0.0001029537,0.0005677293,0.0007379205,0.0009367766],"genre_scores_gemma":[0.9475875,0.0002580678,0.05066914,0.000114433,0.00007611399,0.0001434936,0.0005985913,0.00003335143,0.0005192274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003637438,"threshold_uncertainty_score":0.0192368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471055587510444,"score_gpt":0.2460454903493684,"score_spread":0.231334934474264,"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."}}