{"id":"W3210061252","doi":"10.3390/ijms222111734","title":"Enhancing the Efficiency of Distraction Osteogenesis through Rate-Varying Distraction: A Computational Study","year":2021,"lang":"en","type":"article","venue":"International Journal of Molecular Sciences","topic":"Bone fractures and treatments","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Shriners Hospitals for Children - Canada","funders":"Fonds de Recherche du Québec - Santé; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China; Shriners Hospitals for Children","keywords":"Distraction; Distraction osteogenesis; Bone formation; Soft tissue; Medicine; Bone healing; Orthodontics; Biomedical engineering; Surgery; Psychology","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":[0.000340051,0.0004656376,0.0006633767,0.0005075541,0.0003108685,0.0006826623,0.0008398716,0.0009532861,0.001069354],"category_scores_gemma":[0.001162882,0.000259056,0.0006809819,0.0004345075,0.0003980229,0.0004086915,0.0004024954,0.0003858222,0.00007667825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005153411,"about_ca_system_score_gemma":0.0007655465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008953859,"about_ca_topic_score_gemma":0.005182542,"domain_scores_codex":[0.9999082,0.00002188373,0.000007713706,0.0000172782,0.00002276907,0.00002206262],"domain_scores_gemma":[0.9991814,0.0006092686,0.00007844144,0.00003442303,0.00006773628,0.00002862795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009076012,0.0001438832,0.001749007,0.0001018315,0.00001907821,0.00008019879,0.00002499386,0.990019,0.002085445,0.001121311,0.0001334923,0.00443092],"study_design_scores_gemma":[0.00001071317,0.00003371646,0.00021534,0.00000385803,0.000009985585,0.00001093926,0.00001017955,0.9989879,0.000446974,0.0001350592,0.0001311593,0.000004217434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9425864,0.0008381757,0.04723459,0.0003724722,0.00006955593,0.00008913493,0.0003225784,0.0001224623,0.008364673],"genre_scores_gemma":[0.9841701,0.0003763721,0.01450466,0.00003829725,0.00001068945,0.00006836123,0.0001093979,0.00001599228,0.0007063161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008953859,"threshold_uncertainty_score":0.01780349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193518685541048,"score_gpt":0.3427537072760409,"score_spread":0.3208185204206304,"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."}}