{"id":"W4404624384","doi":"10.2196/59396","title":"A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dementia; Random forest; Gradient boosting; Machine learning; Logistic regression; Artificial intelligence; Receiver operating characteristic; AdaBoost; Support vector machine; Computer science; Medicine; Gerontology; Algorithm; Disease","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.01068205,0.001136755,0.001836935,0.001854348,0.0007890756,0.001186095,0.002010259,0.001244793,0.002700022],"category_scores_gemma":[0.01848483,0.0006724528,0.001793693,0.001317512,0.0003392466,0.0008491603,0.001736914,0.002892326,0.0008115805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001127519,"about_ca_system_score_gemma":0.003323671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01586192,"about_ca_topic_score_gemma":0.009279544,"domain_scores_codex":[0.9980347,0.001099663,0.0001486976,0.0003659533,0.0002129187,0.0001380251],"domain_scores_gemma":[0.9911965,0.006419159,0.0003655795,0.0002600524,0.001602385,0.0001562983],"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":[0.0006720742,0.000597324,0.03566396,0.00021214,0.0006048985,0.0002088441,0.0001268199,0.6937522,0.0008367684,0.002711164,0.006339327,0.2582745],"study_design_scores_gemma":[0.00003748275,0.00004595449,0.0008535495,0.00001052498,0.00002449808,0.00001850172,0.0000100343,0.9978783,0.00007721579,0.0008315492,0.0002061542,0.0000062956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09635666,0.001408156,0.8953119,0.00109665,0.0001701399,0.0006697295,0.001068226,0.002897449,0.001021094],"genre_scores_gemma":[0.5549632,0.0007852722,0.4378278,0.000362831,0.0001743788,0.00194706,0.002514062,0.0001531477,0.001272301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01586192,"threshold_uncertainty_score":0.05649281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0254924248090569,"score_gpt":0.336724110635829,"score_spread":0.3112316858267721,"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."}}