{"id":"W4417221370","doi":"10.3389/fninf.2025.1668395","title":"Assessing the eligibility of Brainomix e-ASPECTS for acute stroke imaging","year":2025,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Acute stroke; Radiological weapon; Stroke (engine); Medical imaging; Neuroimaging; MEDLINE","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":[0.002147744,0.0003869042,0.0002703,0.002242238,0.0002129322,0.000856682,0.0005285534,0.0004200663,0.001722481],"category_scores_gemma":[0.00959109,0.0001874334,0.0002936333,0.001029945,0.0003952631,0.000637409,0.0005883821,0.0002790018,0.0004772575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003486013,"about_ca_system_score_gemma":0.0004856294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002059509,"about_ca_topic_score_gemma":0.003845339,"domain_scores_codex":[0.9990062,0.0002602639,0.0002178202,0.0001013904,0.0003087714,0.0001055213],"domain_scores_gemma":[0.9943743,0.001552051,0.002234799,0.0002861331,0.001193495,0.0003592483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008455382,0.00001028575,0.9965652,0.00001334586,0.00002031286,0.0001073141,0.00002605637,0.00008175877,0.0001790902,0.00001502638,0.0001941342,0.002703044],"study_design_scores_gemma":[0.00001775326,0.00008954939,0.9957391,0.00002726371,0.00003771636,0.001715273,0.00008955127,0.001194595,0.0004502975,0.00007030898,0.0005619115,0.000006685505],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969832,0.0004339418,0.0005492377,0.00007690713,0.00001213995,0.0000390613,0.0005257563,0.00001603033,0.0013637],"genre_scores_gemma":[0.9975408,0.0002017535,0.00107957,0.0000279083,0.00002755313,0.00003131689,0.0009277887,0.000005337105,0.0001579324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002242238,"threshold_uncertainty_score":0.0113585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01100339158528007,"score_gpt":0.3287100102660457,"score_spread":0.3177066186807656,"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."}}