{"id":"W6981483573","doi":"","title":"Enhancing Alzheimer's prognostic models with cross-domain self-supervised learning and MRI data harmonization","year":2024,"lang":"en","type":"article","venue":"D-Scholarship@Pitt (University of Pittsburgh)","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Canadian Institutes of Health Research; State Government of Victoria; Eli Lilly and Company; Edith Cowan University; Dementia Collaborative Research Centres, Australia; National Institutes of Health; Avid Radiopharmaceuticals; University of Melbourne; National Health and Medical Research Council; Commonwealth Scientific and Industrial Research Organisation; Northern California Institute for Research and Education; University of Southern California; Science and Industry Endowment Fund; Alzheimer's Association","keywords":"Comparability; Consistency (knowledge bases); Deep learning; Harmonization; Context (archaeology); Medical imaging; Magnetic resonance imaging; Field (mathematics); Supervised learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.003744019,0.001026444,0.001034418,0.0009458173,0.0003826498,0.0008039422,0.00152621,0.00113501,0.0008034559],"category_scores_gemma":[0.006362126,0.0005356179,0.001301219,0.000780085,0.0008525362,0.001386665,0.001767077,0.001548046,0.0005333956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005450834,"about_ca_system_score_gemma":0.001061242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00296983,"about_ca_topic_score_gemma":0.003432178,"domain_scores_codex":[0.9987968,0.0004644014,0.00006751715,0.0004017201,0.0001839696,0.00008556541],"domain_scores_gemma":[0.9967349,0.001276773,0.0004067556,0.0007918177,0.0006864161,0.0001032812],"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":[0.0003800789,0.0004832984,0.008317349,0.0001252713,0.0003200252,0.0001713957,0.000288388,0.734306,0.00873951,0.002202833,0.004516031,0.2401498],"study_design_scores_gemma":[0.000009093076,0.00003919006,0.0005061376,0.00000495093,0.00001304317,0.00002650832,0.00001352165,0.9959473,0.001758741,0.001341317,0.00033314,0.000007073515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1010294,0.000460186,0.8941642,0.0003509903,0.00006107167,0.00009134482,0.0001848062,0.002628535,0.001029374],"genre_scores_gemma":[0.8224846,0.0002208359,0.171958,0.0006495265,0.0001292462,0.0001731615,0.001836896,0.0003129222,0.00223479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003744019,"threshold_uncertainty_score":0.01980048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05817080456652605,"score_gpt":0.2302712028150507,"score_spread":0.1721003982485247,"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."}}