{"id":"W4285333132","doi":"10.2196/34821","title":"Development of an Assistive Technology for Cognition to Support Meal Preparation in Severe Traumatic Brain Injury: User-Centered Design Study","year":2022,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université de Sherbrooke; Centre for Interdisciplinary Research in Rehabilitation; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre Hospitalier Universitaire de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Cognition; Assistive technology; Traumatic brain injury; Meal preparation; Meal; Assistive device; Psychology; Physical medicine and rehabilitation; Medicine; Human–computer interaction; Computer science; Neuroscience; Internal medicine; Psychiatry; Food science; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.01743819,0.0008020378,0.0007820209,0.001130551,0.001121911,0.001514663,0.0009595754,0.00106156,0.002088202],"category_scores_gemma":[0.01553196,0.0005471201,0.001193626,0.0004378427,0.0008457727,0.0009606938,0.00121158,0.0007757705,0.0003414036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316668,"about_ca_system_score_gemma":0.002995946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008662104,"about_ca_topic_score_gemma":0.001526769,"domain_scores_codex":[0.9915199,0.006062345,0.0007007635,0.0005358619,0.0006077845,0.0005733615],"domain_scores_gemma":[0.9868732,0.007678383,0.0008177123,0.0008881129,0.002657842,0.001084701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.02161415,0.0978656,0.1682121,0.008812423,0.0009160831,0.002269634,0.1960691,0.01174868,0.05764648,0.004773952,0.00328031,0.4267915],"study_design_scores_gemma":[0.01908099,0.4514485,0.257388,0.001631629,0.002364293,0.001712549,0.1247509,0.04020313,0.05651508,0.004663861,0.03951174,0.0007292974],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815694,0.00008325919,0.01095268,0.00007881957,0.00001919664,0.006446559,0.0001049623,0.00005918657,0.0006858786],"genre_scores_gemma":[0.8832848,0.0002069283,0.09403557,0.0001951819,0.00002663985,0.02099404,0.0002039026,0.00002595448,0.001026908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01743819,"threshold_uncertainty_score":0.09222305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2047488600229966,"score_gpt":0.4460919757319893,"score_spread":0.2413431157089928,"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."}}