{"id":"W2050278510","doi":"10.1016/j.compmedimag.2013.08.005","title":"A local angle compensation method based on kinematics constraints for non-invasive vascular axial strain computations on human carotid arteries","year":2013,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Medicine; Ultrasound; Kinematics; Stenosis; Elastography; Anatomy; Biomedical engineering; Mathematics; Radiology; Physics","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":[0.0003284909,0.0005465975,0.0005063636,0.0004164653,0.0003100581,0.0005615579,0.0006366686,0.0006061751,0.003063411],"category_scores_gemma":[0.0008570195,0.0004068067,0.0003611343,0.0004412645,0.0002385267,0.0005497191,0.0004872472,0.0005454679,0.0006322972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001656788,"about_ca_system_score_gemma":0.0008087931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002681281,"about_ca_topic_score_gemma":0.005538037,"domain_scores_codex":[0.9998234,0.00003314984,0.0000147436,0.00003902707,0.00007527961,0.00001441217],"domain_scores_gemma":[0.9996789,0.0001048008,0.00003892996,0.00004430352,0.0001081139,0.00002500405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002766351,0.0001677381,0.001196725,0.0002470693,0.00006153109,0.0001509193,0.00014233,0.1868222,0.1765055,0.006854307,0.001746286,0.6258287],"study_design_scores_gemma":[0.00002235656,0.0001060839,0.0009448704,0.00001323768,0.0000227064,0.00008499034,0.00002665728,0.9799529,0.0158725,0.0008712198,0.002060367,0.00002217046],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01062122,0.00006455523,0.988605,0.00002176794,0.00001872845,0.00001944014,0.0000165923,0.0002361578,0.0003965085],"genre_scores_gemma":[0.2159722,0.000177287,0.7810238,0.00003599126,0.00003444603,0.00008899865,0.0001116491,0.0001960664,0.002359667],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003063411,"threshold_uncertainty_score":0.01024812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177145845842777,"score_gpt":0.2991867574167089,"score_spread":0.2814721728324312,"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."}}