{"id":"W4297229296","doi":"10.24297/ijct.v22i.9288","title":"In-silico patient-specific and patient-appropriate engineering method to judiciously select an ameliorative implant design in a single-patient using finite element-n-of-1 (fe-n-of-1) empirical test analysis to reconstruct mid-sagittal osteochondrotomy of the sternum following cardiac surgery","year":2022,"lang":"en","type":"article","venue":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","topic":"Orthopaedic implants and arthroplasty","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Health Sciences","funders":"","keywords":"Finite element method; Implant; Test (biology); Sternum; Computer science; Test case; Protocol (science); Empirical research; Structural engineering; Engineering; Medicine; Surgery; Mathematics; Machine learning; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004954724,0.0001817464,0.0008234233,0.002635693,0.00003654496,0.00001329499,0.0002293018,0.00006534452,0.000006475184],"category_scores_gemma":[0.0001728649,0.0001494997,0.0002624174,0.001421296,0.00005528246,0.0001104949,0.0002929892,0.0003569015,8.628976e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002366203,"about_ca_system_score_gemma":0.0001585889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003646779,"about_ca_topic_score_gemma":0.00000816033,"domain_scores_codex":[0.9974712,0.0002523811,0.001202903,0.0002465254,0.0005930035,0.0002339951],"domain_scores_gemma":[0.9981114,0.0006443005,0.0007293031,0.0001700043,0.000261917,0.00008302627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001019948,0.0008905805,0.6003217,0.00002451118,0.001624725,0.0007422191,0.006306705,0.08631746,0.09393802,0.00001592775,0.00007395128,0.2087243],"study_design_scores_gemma":[0.01196091,0.03701838,0.1729482,0.004308686,0.002247545,0.01120001,0.01861168,0.08098555,0.6524891,0.0006016019,0.005386201,0.002242175],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9617349,0.0001351509,0.03631686,0.0003488825,0.0009658018,0.00042751,0.00006158627,0.000007747952,0.000001542562],"genre_scores_gemma":[0.9677278,0.000006563869,0.03208629,0.000119718,0.00002846264,0.00001141107,0.000004768063,0.00001468842,2.317302e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.558551,"threshold_uncertainty_score":0.609642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02064956318631478,"score_gpt":0.2867873549958081,"score_spread":0.2661377918094933,"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."}}