{"id":"W4400724407","doi":"10.1162/imag_a_00251","title":"Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching","year":2024,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital","funders":"Medical Research Council; McDonnell Center for Systems Neuroscience; National Supercomputing Centre Singapore; National Institutes of Health; National Medical Research Council; Temasek Foundation","keywords":"Matching (statistics); Computer science; Meta-analysis; Artificial intelligence; Big data; Pattern recognition (psychology); Data mining; Statistics; Mathematics; Medicine; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008393093,0.0002378433,0.0003733803,0.0002722202,0.0002419695,0.0004069925,0.000574859,0.00003704558,0.00001585322],"category_scores_gemma":[0.0003790458,0.0001989874,0.0001170577,0.0006711177,0.0001593936,0.0005969627,0.0002880824,0.000716324,0.00000866951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007266651,"about_ca_system_score_gemma":0.0002184998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007939145,"about_ca_topic_score_gemma":0.000008501513,"domain_scores_codex":[0.9973468,0.000087883,0.0004003601,0.001157974,0.0005329233,0.000474046],"domain_scores_gemma":[0.9986644,0.0001759809,0.00004943139,0.0007299385,0.00003627143,0.000343927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004443076,0.0001011202,0.002776546,0.0001673347,0.0001372934,0.0006978564,0.002224847,0.05788089,0.6963948,0.0005244346,0.0004472323,0.2386032],"study_design_scores_gemma":[0.0002084614,0.0000182291,0.0008163755,0.000235068,0.0006189547,0.0001896795,0.0000417524,0.9934337,0.0003521755,0.001388052,0.002529298,0.0001682972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.217199,0.001230462,0.7700912,0.008667409,0.001707677,0.0002447515,0.00008480553,0.0003997817,0.0003749813],"genre_scores_gemma":[0.9490103,0.00002531454,0.04668754,0.00370174,0.000430903,0.000006273759,0.00003329636,0.00005717881,0.00004747874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9355528,"threshold_uncertainty_score":0.8114471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.127420788564673,"score_gpt":0.350913189286187,"score_spread":0.223492400721514,"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."}}