{"id":"W2026846414","doi":"10.1002/jmri.22872","title":"Water‐silicone separated volumetric MR acquisition for rapid assessment of breast implants","year":2012,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Breast Implant and Reconstruction","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Silicone; Medicine; Biomedical engineering; Breast MRI; Nuclear medicine; Radiology; Materials science; Breast cancer; Mammography; Internal medicine; Composite material","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":[0.001252596,0.0008223185,0.0004731753,0.0006642985,0.0001181944,0.0005790005,0.0006872797,0.0005358536,0.001357217],"category_scores_gemma":[0.001821574,0.000449321,0.0002888597,0.0002771573,0.0003446034,0.0006824915,0.000680057,0.000469802,0.0006142966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001680649,"about_ca_system_score_gemma":0.0006175181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001957913,"about_ca_topic_score_gemma":0.0003578683,"domain_scores_codex":[0.9996612,0.00009976267,0.00002178222,0.00006363341,0.0001178464,0.00003579273],"domain_scores_gemma":[0.9994971,0.0001811302,0.00009157137,0.00005970773,0.0001281193,0.0000423348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002776893,0.00003793495,0.00105761,0.0002572238,0.00001930913,0.0001766874,0.00003788412,0.0008648178,0.9309034,0.0002627834,0.0003663773,0.06573838],"study_design_scores_gemma":[0.00008450788,0.001443258,0.01278583,0.0001163112,0.0001564121,0.008253274,0.00009198912,0.04143553,0.9235992,0.0007437059,0.01119744,0.00009244715],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2278653,0.00730532,0.7603087,0.0003768888,0.0001472369,0.0003486917,0.0002843829,0.001732976,0.001630526],"genre_scores_gemma":[0.4241662,0.00347928,0.5692428,0.0003149231,0.0001737915,0.0003466578,0.0004117692,0.0004477674,0.001416702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001357217,"threshold_uncertainty_score":0.006624401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287220787041334,"score_gpt":0.2903837538229962,"score_spread":0.2775115459525829,"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."}}