{"id":"W1966750465","doi":"10.1089/ten.tec.2014.0409","title":"Magnetic Resonance Imaging of Human Tissue-Engineered Adipose Substitutes","year":2014,"lang":"en","type":"article","venue":"Tissue Engineering Part C Methods","topic":"Mesenchymal stem cell research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier universitaire de Québec; Université Laval","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Canadian Federation of University Women","keywords":"Magnetic resonance imaging; Adipose tissue; Biomedical engineering; In vivo; Perfusion; Resorption; Connective tissue; Nuclear magnetic resonance; Chemistry; Medicine; Radiology; Pathology; Biology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003743831,0.0002692716,0.0001630027,0.0002260109,0.00005427984,0.0001470015,0.0001312162,0.0002364925,0.0006332018],"category_scores_gemma":[0.000200222,0.00009779679,0.0001227959,0.0001635152,0.0001282352,0.0001325638,0.0001325113,0.0001796814,0.0002792237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006550804,"about_ca_system_score_gemma":0.00008745678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002471289,"about_ca_topic_score_gemma":0.0001937539,"domain_scores_codex":[0.9998854,0.00003967177,0.000006457909,0.00002188031,0.00003442867,0.00001211181],"domain_scores_gemma":[0.9998976,0.00002736491,0.00002240903,0.00001273139,0.00002589403,0.00001407567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001201726,0.00001166971,0.0001590199,0.00003925155,0.000004630561,0.00008142772,0.00002502931,0.0001691867,0.9975023,0.0000387873,0.00002771044,0.001820834],"study_design_scores_gemma":[0.00001242146,0.0006103761,0.003991711,0.00001623425,0.00003674706,0.0007537904,0.00006999364,0.002618353,0.9875207,0.00004158808,0.004318958,0.000009008269],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746521,0.004042624,0.01879395,0.00005995495,0.00003455637,0.00004762924,0.000225039,0.0001149913,0.002029231],"genre_scores_gemma":[0.9745396,0.002967088,0.01765255,0.0001026222,0.00002082054,0.00008108091,0.0006416516,0.00004747963,0.003947133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006332018,"threshold_uncertainty_score":0.002118289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03716694983057268,"score_gpt":0.3754422846396237,"score_spread":0.338275334809051,"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."}}