{"id":"W2518344761","doi":"10.1118/1.4961823","title":"Poster ‐ 49: Assessment of Synchrony respiratory compensation error for CyberKnife liver treatment","year":2016,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Ottawa Hospital","funders":"","keywords":"Cyberknife; Fiducial marker; Percentile; Nuclear medicine; Compensation (psychology); Respiratory compensation; Motion compensation; Computer science; Tracking error; Tracking (education); Medicine; Radiation therapy; Mathematics; Statistics; Radiosurgery; Computer vision; Radiology; Artificial intelligence","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":[0.001538548,0.0003246079,0.0003484677,0.0007592389,0.0002132585,0.0005892007,0.0002843536,0.0003201789,0.001782832],"category_scores_gemma":[0.006449028,0.0001421037,0.0004121089,0.0004755605,0.0002613395,0.0003817192,0.0006278223,0.0002839324,0.0003958798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005071604,"about_ca_system_score_gemma":0.0004283224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001523582,"about_ca_topic_score_gemma":0.001455992,"domain_scores_codex":[0.9989868,0.0002160348,0.0001161001,0.0001566213,0.0004749242,0.00004950839],"domain_scores_gemma":[0.9969733,0.001186674,0.0009081159,0.0004202023,0.000433129,0.00007861092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003092215,0.0001826289,0.5691556,0.0002901554,0.0002966662,0.0003735004,0.0005533507,0.09108999,0.06878069,0.0004589955,0.0009330802,0.2647932],"study_design_scores_gemma":[0.000068536,0.002119475,0.7493445,0.00004840872,0.0001898037,0.001600022,0.0002113938,0.1267736,0.1166463,0.0003988905,0.002510771,0.00008829822],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608619,0.0005750594,0.03610187,0.00008116098,0.000030573,0.00005771097,0.0004692455,0.0003462024,0.00147627],"genre_scores_gemma":[0.9924259,0.00008520151,0.006661643,0.00001384334,0.000009962581,0.00002028231,0.0002692953,0.00006227332,0.0004515978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001782832,"threshold_uncertainty_score":0.00813669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02833232005486542,"score_gpt":0.3431486318198973,"score_spread":0.3148163117650319,"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."}}