{"id":"W3094447715","doi":"10.1016/j.media.2020.101872","title":"Unifying neural learning and symbolic reasoning for spinal medical report generation","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Domain (mathematical analysis); Machine learning; Deep learning; Artificial neural network; Segmentation; Graph; Theoretical computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001986705,0.000851661,0.0009987097,0.001936953,0.0006972124,0.002612332,0.003041741,0.001481658,0.007354483],"category_scores_gemma":[0.008027521,0.0005515897,0.001875582,0.001274664,0.001189069,0.004138902,0.00284983,0.001835133,0.001689875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001506541,"about_ca_system_score_gemma":0.002961697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01192102,"about_ca_topic_score_gemma":0.02252729,"domain_scores_codex":[0.9983336,0.0003048294,0.0002178146,0.0003859561,0.000573418,0.0001844539],"domain_scores_gemma":[0.9951847,0.002689166,0.0003077549,0.001004146,0.0006840178,0.0001301657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004089979,0.0003957883,0.002650039,0.0003894957,0.0001661918,0.0003613899,0.0002799411,0.2209182,0.009253876,0.04903443,0.007381795,0.7087599],"study_design_scores_gemma":[0.0000187673,0.00003755316,0.0002033975,0.00003434775,0.00003936964,0.00004996315,0.00003576281,0.9376065,0.004981789,0.05512014,0.00185769,0.00001464437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02519803,0.0004658502,0.9620746,0.0006327262,0.00009982815,0.0001688007,0.0006291257,0.007254027,0.003476878],"genre_scores_gemma":[0.4448264,0.000406909,0.5479836,0.0002967039,0.0001086254,0.0001340548,0.001936774,0.0004035632,0.003903327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01192102,"threshold_uncertainty_score":0.02460313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925856031216853,"score_gpt":0.2978229118212617,"score_spread":0.2785643515090932,"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."}}