{"id":"W4403909094","doi":"10.1016/j.media.2024.103381","title":"A cross-attention-based deep learning approach for predicting functional stroke outcomes using 4D CTP imaging and clinical metadata","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"","keywords":"Metadata; Computer science; Artificial intelligence; Deep learning; Stroke (engine); Machine learning; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002571518,0.000282908,0.0008212251,0.0006046042,0.0002924614,0.0004895927,0.0001726858,0.0001461321,0.000621283],"category_scores_gemma":[0.00317309,0.0002266058,0.0009615607,0.000778962,0.000473958,0.0003822933,0.0002267788,0.0007090207,0.000004984281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001105757,"about_ca_system_score_gemma":0.0001755949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008855351,"about_ca_topic_score_gemma":0.000006820392,"domain_scores_codex":[0.9964513,0.0001229694,0.0009183218,0.0009188763,0.001141007,0.0004475767],"domain_scores_gemma":[0.9979482,0.0009268271,0.0001558055,0.0003835858,0.0001898427,0.000395704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000757067,0.0001721325,0.9723781,0.0003590361,0.006906698,0.0001582161,0.00002428717,0.0005971813,0.0003845158,0.00001951978,0.0005056728,0.01841888],"study_design_scores_gemma":[0.001520817,0.00004162996,0.06336987,0.00005868557,0.01449448,0.0000255836,0.0002777904,0.9184073,0.00002997856,0.000002780799,0.001593907,0.0001771287],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1548866,0.0007980064,0.8418891,0.001231908,0.0001736663,0.0003005863,0.00004068768,0.0001892378,0.0004902103],"genre_scores_gemma":[0.8755023,0.00003211561,0.1178113,0.001081298,0.0007399982,0.00006861849,0.00128931,0.00006689267,0.003408212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9178101,"threshold_uncertainty_score":0.9240718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03386706435013236,"score_gpt":0.3710186549519243,"score_spread":0.337151590601792,"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."}}