{"id":"W2052973549","doi":"10.1016/j.brachy.2014.02.289","title":"Validation of the Oncentrabrachy Collapsed Cone Convolution Algorithm Using Real Patient Heterogeneous Geometries","year":2014,"lang":"en","type":"article","venue":"Brachytherapy","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre hospitalier universitaire de Québec","funders":"","keywords":"Radiation oncologist; Nuclear medicine; Medicine; DICOM; Imaging phantom; Monte Carlo method; Dosimetry; Convolution (computer science); Brachytherapy; Radiation treatment planning; Histogram; Algorithm; Medical physics; Radiation therapy; Computer science; Mathematics; Radiology; Artificial intelligence; Statistics; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001305069,0.0001616122,0.0002333051,0.00005998019,0.0001569392,0.00002348034,0.0001506505,0.00005039966,0.0001172992],"category_scores_gemma":[0.000005603139,0.0001277549,0.0001195102,0.0002884503,0.000132349,0.0001285231,0.00002203222,0.000092444,7.791135e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007211677,"about_ca_system_score_gemma":0.00004391631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002228529,"about_ca_topic_score_gemma":0.000001022016,"domain_scores_codex":[0.9989163,0.0001276097,0.0003117423,0.0002073047,0.0002249828,0.0002121094],"domain_scores_gemma":[0.9990716,0.00007407522,0.0003580506,0.0003434043,0.0001137078,0.00003918575],"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":[0.0001239004,0.0004116331,0.06192405,0.00001814229,0.0002614743,5.791762e-7,0.001023243,0.007389223,0.2831933,0.006225741,0.0001221513,0.6393065],"study_design_scores_gemma":[0.00106594,0.0003147411,0.001977416,0.00003969403,0.00002570232,0.000004854331,0.00005676963,0.01782188,0.9683556,0.003638239,0.006413945,0.000285242],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6159912,0.00009688892,0.3831712,0.00002719556,0.0001587053,0.000322453,0.0000339743,0.00004260581,0.0001557638],"genre_scores_gemma":[0.9787834,0.00003699473,0.02081291,0.0000818476,0.0001777003,0.00002143262,0.00002247261,0.00003092281,0.00003230041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6851623,"threshold_uncertainty_score":0.5209694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008910714079841191,"score_gpt":0.2519476140691017,"score_spread":0.2430368999892605,"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."}}