{"id":"W2020774554","doi":"10.1118/1.3181890","title":"SU‐FF‐T‐408: Tissue Inhomogeneities in Monte Carlo Treatment Planning for Proton Therapy","year":2009,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Maisonneuve-Rosemont","funders":"","keywords":"Proton therapy; Monte Carlo method; Imaging phantom; Voxel; Hounsfield scale; Nuclear medicine; Materials science; Artifact (error); Segmentation; Proton; Streaking; Radiation treatment planning; Physics; Energy (signal processing); Biomedical engineering; Beam (structure); Optics; Computer science; Artificial intelligence; Mathematics; Computed tomography; Medicine; Radiology; Radiation therapy; Nuclear physics; Statistics","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":[0.00119684,0.0003791998,0.0002348589,0.0005489353,0.000182246,0.0005319192,0.0005199101,0.0004700206,0.001062727],"category_scores_gemma":[0.005001782,0.0004534702,0.0002542827,0.0004832061,0.0002945715,0.000321469,0.0002945443,0.0003300555,0.000339702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007485666,"about_ca_system_score_gemma":0.0005677487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003826231,"about_ca_topic_score_gemma":0.003376431,"domain_scores_codex":[0.9995269,0.0002088079,0.0000186813,0.00003153373,0.0001961835,0.00001803497],"domain_scores_gemma":[0.9993387,0.0004229865,0.00008457577,0.00005239588,0.00008322033,0.00001809036],"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":[0.001370254,0.00007478525,0.01104584,0.000428227,0.0001881175,0.0002818296,0.0002917798,0.7111942,0.09321539,0.003801743,0.002550854,0.1755571],"study_design_scores_gemma":[0.00006559488,0.0002510924,0.01011035,0.00003995523,0.000070468,0.0006758734,0.0000235918,0.9408857,0.04130441,0.002577092,0.003951745,0.00004404588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1627828,0.001923891,0.8284585,0.0002025367,0.00004162025,0.0001544083,0.0001851467,0.002267807,0.003983168],"genre_scores_gemma":[0.8143328,0.000408825,0.1822255,0.0001101535,0.00001584863,0.0001478224,0.0002408248,0.0008061828,0.00171208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003826231,"threshold_uncertainty_score":0.007607937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02238343315062185,"score_gpt":0.2962310319738665,"score_spread":0.2738475988232446,"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."}}