{"id":"W2542170102","doi":"10.1016/j.clinbiomech.2016.10.015","title":"Identification of potential compensatory muscle strategies in a breast cancer survivor population: A combined computational and experimental approach","year":2016,"lang":"en","type":"article","venue":"Clinical Biomechanics","topic":"Lymphatic System and Diseases","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"Pectoralis major muscle; Breast cancer; Population; Generalizability theory; Pectoralis Muscle; Physical medicine and rehabilitation; Constraint (computer-aided design); Medicine; Cancer; Computer science; Internal medicine; Pathology; Surgery; Mathematics; Statistics","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":[0.0004945712,0.0002086333,0.0003473097,0.0003588688,0.0002180495,0.0004979959,0.0005837156,0.0005125243,0.002413033],"category_scores_gemma":[0.00204255,0.0002463019,0.0003272051,0.0001921835,0.0004448595,0.0003816334,0.0004847347,0.0004169746,0.0001864722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002318057,"about_ca_system_score_gemma":0.0005744014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002683162,"about_ca_topic_score_gemma":0.002751436,"domain_scores_codex":[0.9999179,0.00003306006,0.000005076794,0.00001715832,0.00001107403,0.00001564309],"domain_scores_gemma":[0.9994118,0.0004254239,0.00004698316,0.00004892046,0.00004147029,0.00002545474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002744322,0.002664802,0.2983697,0.0005185368,0.0003163595,0.0007800405,0.001122766,0.4238807,0.1562748,0.01019899,0.001448453,0.1016806],"study_design_scores_gemma":[0.00006311416,0.0008263009,0.08684745,0.0000209743,0.00006165535,0.0004350336,0.0007531815,0.9020767,0.005318581,0.003200293,0.000369509,0.00002719839],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845963,0.00004168108,0.01442318,0.00013698,0.000005317465,0.00003672026,0.0002118351,0.00002967714,0.0005183385],"genre_scores_gemma":[0.9936007,0.00005153306,0.005701685,0.00003169039,0.000004378041,0.00009134047,0.0002302815,0.000004993524,0.0002834775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002683162,"threshold_uncertainty_score":0.008072376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03249567249896858,"score_gpt":0.3452980576232064,"score_spread":0.3128023851242379,"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."}}