{"id":"W1996063407","doi":"10.1080/10255842.2011.648625","title":"<i>In vivo</i>monitoring of bone–implant bond strength by microCT and finite element modelling","year":2012,"lang":"en","type":"article","venue":"Computer Methods in Biomechanics & Biomedical Engineering","topic":"Orthopaedic implants and arthroplasty","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Calgary","funders":"Canadian Institutes of Health Research; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Heritage Medical Research Institute","keywords":"Implant; Fixation (population genetics); Finite element method; Stiffness; Biomedical engineering; Materials science; Tibia; Bone resorption; In vivo; Bone remodeling; Implant failure; Biomechanics; Orthodontics; Dentistry; Medicine; Surgery; Structural engineering; Composite material; Anatomy; Engineering","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":[0.0006516612,0.0004718191,0.000324042,0.0006716086,0.0001737267,0.0004629589,0.0004748647,0.0004722272,0.00338566],"category_scores_gemma":[0.001031194,0.0002501701,0.0003168707,0.0004265373,0.0004766842,0.0003391989,0.0002851738,0.0003170007,0.0005492431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000433431,"about_ca_system_score_gemma":0.0002607832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002026036,"about_ca_topic_score_gemma":0.003390591,"domain_scores_codex":[0.9995345,0.0001075113,0.00003689143,0.00008059251,0.0002128498,0.00002767555],"domain_scores_gemma":[0.9994758,0.0002100425,0.0001111566,0.00008432955,0.0001066128,0.00001201463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001540331,0.00007734968,0.002981925,0.0002450149,0.00001885319,0.0001148069,0.0001369174,0.02102667,0.9464853,0.001494934,0.000694544,0.02656961],"study_design_scores_gemma":[0.00001227992,0.0002033006,0.01137643,0.00002255445,0.00002830548,0.0002567062,0.00006090083,0.2293364,0.7541193,0.0005769625,0.003949475,0.00005736565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3525502,0.001035451,0.6374573,0.0002595145,0.0001071216,0.00019851,0.001551236,0.001789876,0.005050825],"genre_scores_gemma":[0.7499342,0.000822799,0.2447574,0.00006931947,0.00003038358,0.0004238188,0.0005031679,0.0001601765,0.003298666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00338566,"threshold_uncertainty_score":0.01132619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197556410066321,"score_gpt":0.3016405359107391,"score_spread":0.281884894904107,"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."}}