{"id":"W4414370937","doi":"10.1007/978-3-032-05182-0_3","title":"Attention-Based Multimodal Deep Learning Model for Post-stroke Motor Impairment Prediction","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Jewish Rehabilitation Hospital; McGill University","funders":"","keywords":"Diffusion MRI; Neuroimaging; Deep learning; Pooling; Fractional anisotropy; Residual; Pattern recognition (psychology); White matter","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":[0.0003624347,0.0008044159,0.0006836421,0.0004467685,0.0001820914,0.0003850959,0.0008825521,0.0007199734,0.003354461],"category_scores_gemma":[0.0006538237,0.0002345326,0.0007674975,0.0004437725,0.0001261331,0.0005240816,0.0006877119,0.00114948,0.001024009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005008584,"about_ca_system_score_gemma":0.000565881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01265683,"about_ca_topic_score_gemma":0.01634279,"domain_scores_codex":[0.9998937,0.0000134033,0.000005413038,0.00003776318,0.00001791466,0.00003193767],"domain_scores_gemma":[0.9998517,0.00006014057,0.00000988029,0.00001005297,0.00005748841,0.00001079364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003139994,0.0002839568,0.003235918,0.0001001239,0.0001613312,0.0001629365,0.00004810526,0.2088477,0.008246559,0.001461548,0.01365064,0.7634873],"study_design_scores_gemma":[0.00000421496,0.00005222126,0.001281162,0.00001485052,0.00003726818,0.00003701153,0.000006920348,0.9952391,0.001372689,0.001418614,0.0005277708,0.000008179927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1370542,0.008465545,0.8363817,0.001227372,0.000722369,0.000110991,0.002988733,0.003768639,0.009280451],"genre_scores_gemma":[0.9206809,0.001946347,0.0541473,0.0004830453,0.0002465632,0.0001481964,0.002463425,0.0001086787,0.01977552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01265683,"threshold_uncertainty_score":0.02516633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00959985440567102,"score_gpt":0.2552717795148176,"score_spread":0.2456719251091466,"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."}}