{"id":"W3090387320","doi":"10.1016/j.nmd.2020.08.217","title":"DMD – ANIMAL MODELS & PRECLINICAL TREATMENT","year":2020,"lang":"en","type":"article","venue":"Neuromuscular Disorders","topic":"Animal testing and alternatives","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"AGADA Biosciences","funders":"","keywords":"Normative; Medicine; Consistency (knowledge bases); Regression; Forelimb; mdx mouse; Linear regression; Grip strength; Preclinical research; Benchmark (surveying); Body weight; Internal medicine; Duchenne muscular dystrophy; Physiology; Dystrophin; Statistics; Computer science; Medical physics; Mathematics; Artificial intelligence","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":[0.001823083,0.0007847703,0.0005259078,0.0011872,0.0005088685,0.001025616,0.0009441319,0.001913849,0.01942468],"category_scores_gemma":[0.0008786182,0.000218374,0.0004456895,0.0004023789,0.001453498,0.0009696356,0.001001736,0.002119079,0.005146225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005019342,"about_ca_system_score_gemma":0.0006611752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004023908,"about_ca_topic_score_gemma":0.0009496273,"domain_scores_codex":[0.9992478,0.0002066404,0.00004707308,0.0000780013,0.0003476052,0.0000728848],"domain_scores_gemma":[0.9994336,0.0001250653,0.0001168795,0.00008389692,0.0001033516,0.0001372946],"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":[0.003893307,0.003178834,0.001579959,0.00474941,0.0001107304,0.002737619,0.0002727569,0.001501634,0.4446386,0.0478075,0.05466355,0.4348661],"study_design_scores_gemma":[0.0003357036,0.012491,0.005049862,0.002293477,0.000183354,0.0112132,0.0002703251,0.001094324,0.1602447,0.01797187,0.7887698,0.00008227218],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2069187,0.2490637,0.1032895,0.0263612,0.007451571,0.003235677,0.005738464,0.002063398,0.3958778],"genre_scores_gemma":[0.4630975,0.1962088,0.09341779,0.00938352,0.003619831,0.002273399,0.005714461,0.0004464957,0.2258383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01942468,"threshold_uncertainty_score":0.064982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3626376224639702,"score_gpt":0.4209730568934479,"score_spread":0.05833543442947764,"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."}}