{"id":"W3170546833","doi":"10.1681/asn.2021050654","title":"Too Little or Too Much? Extracellular Matrix Remodeling in Kidney Health and Disease","year":2021,"lang":"en","type":"editorial","venue":"Journal of the American Society of Nephrology","topic":"Tissue Engineering and Regenerative Medicine","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Translational Research in Oncology; Toronto General Hospital; University Health Network","funders":"Canada Foundation for Innovation; Kidney Foundation of Canada","keywords":"Extracellular matrix; Extracellular; Cell biology; Disease; Matrix (chemical analysis); Kidney disease; Chemistry; Biology; Pathology; Medicine; Endocrinology","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":[0.005923191,0.002811232,0.00325858,0.002121569,0.00286733,0.005011856,0.002815284,0.009980856,0.0110264],"category_scores_gemma":[0.01220941,0.0008208299,0.001405261,0.001016267,0.002647022,0.005123418,0.001628364,0.01706666,0.006489785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002313913,"about_ca_system_score_gemma":0.003056292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001598799,"about_ca_topic_score_gemma":0.005089679,"domain_scores_codex":[0.9976702,0.0005667548,0.0002914408,0.0002614829,0.001050506,0.0001595763],"domain_scores_gemma":[0.9885263,0.005697102,0.0004866884,0.0002144154,0.003119873,0.001955533],"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":[0.00005248467,0.0000119478,0.00002970094,0.0003334384,0.00001217036,0.00009604848,0.00002250917,0.00001979332,0.00005458651,0.0007304162,0.9858754,0.01276134],"study_design_scores_gemma":[0.00006932427,0.00005146576,0.0002266179,0.000798761,0.00005067685,0.0003439236,0.00007599911,0.0001166213,0.00009033323,0.0019584,0.9961964,0.00002157158],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0000492456,0.02225149,0.0001209415,0.04339211,0.9325926,0.00001379181,0.00003654917,0.00005450549,0.001488786],"genre_scores_gemma":[0.0003318964,0.01275566,0.00008298764,0.01625289,0.9672446,0.000009827953,0.00001586385,0.0000185003,0.003287725],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.0110264,"threshold_uncertainty_score":0.03688705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01569823771534978,"score_gpt":0.3158711315594683,"score_spread":0.3001728938441185,"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."}}