{"id":"W4320001167","doi":"10.1016/b978-0-323-99924-3.00001-7","title":"Magnetic resonance imaging in personalized medicine","year":2023,"lang":"en","type":"book-chapter","venue":"Metabolomics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Personalized medicine; Magnetic resonance imaging; Medicine; Medical imaging; Disease; Drug delivery; Precision medicine; Medical physics; Risk stratification; Intensive care medicine; Pathology; Bioinformatics; Radiology; Nanotechnology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000469462,0.000940192,0.0006617614,0.001432071,0.0006112628,0.002616766,0.000797805,0.001811702,0.05088642],"category_scores_gemma":[0.0008885844,0.0003365016,0.0003246175,0.001458641,0.001053279,0.003126016,0.001378005,0.002485292,0.03263595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009242861,"about_ca_system_score_gemma":0.0006954382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00098499,"about_ca_topic_score_gemma":0.00337887,"domain_scores_codex":[0.9997436,0.00006012145,0.000008725687,0.00002902513,0.0001427575,0.0000157149],"domain_scores_gemma":[0.9997328,0.0001640458,0.00001097792,0.00002424094,0.00004879226,0.00001922819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001444359,0.00003523156,0.00004908492,0.000467891,0.00001023838,0.0001225434,0.0001978153,0.0005308812,0.0008678856,0.1552722,0.4507742,0.3916577],"study_design_scores_gemma":[0.000001436706,0.000005556553,0.00005852656,0.0001967361,0.00000299894,0.0002671645,0.00003425124,0.0001589655,0.0001540121,0.03650384,0.962611,0.00000556533],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.0004766637,0.1774851,0.03019712,0.008522014,0.007684068,0.00005475068,0.0002429545,0.0004803885,0.7748569],"genre_scores_gemma":[0.003084743,0.07725686,0.01236962,0.005166159,0.00346928,0.00006086195,0.0001646815,0.0002756272,0.8981521],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05088642,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03610264756952559,"score_gpt":0.3068335451056743,"score_spread":0.2707308975361487,"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."}}