{"id":"W2064911381","doi":"10.1120/jacmp.v7i2.2210","title":"PTV margin for dose‐escalated radiation therapy of prostate cancer with daily online realignment using internal fiducial markers: Monte Carlo approach and dose population histogram (DPH) analysis","year":2006,"lang":"en","type":"article","venue":"Journal of Applied Clinical Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"","keywords":"Fiducial marker; Nuclear medicine; Medicine; Prostate cancer; Margin (machine learning); Radiation therapy; Prostate; Radiation treatment planning; Dose-volume histogram; Rectum; Isocenter; Dosimetry; Radiology; Cancer; Surgery; Computer science; Internal medicine","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":[0.000726751,0.0002586369,0.0003366896,0.0006315727,0.0001695877,0.0004888768,0.000514251,0.0002856606,0.001173534],"category_scores_gemma":[0.001270612,0.0003392989,0.0005901564,0.0004427879,0.0001653824,0.0002314109,0.0003579357,0.0003670736,0.000214747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001117102,"about_ca_system_score_gemma":0.0005988708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002994809,"about_ca_topic_score_gemma":0.00265155,"domain_scores_codex":[0.9997188,0.0001169726,0.00001163835,0.00002776235,0.0001069696,0.00001767765],"domain_scores_gemma":[0.9996334,0.0001867611,0.0000712387,0.00004668345,0.00004873919,0.00001312464],"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":[0.0004929854,0.000131774,0.00785106,0.000227221,0.0001780999,0.0001191535,0.0001759232,0.8376849,0.03845063,0.004200546,0.0008676087,0.1096202],"study_design_scores_gemma":[0.00003524767,0.0001432088,0.00654667,0.000009563038,0.00005046103,0.0001446656,0.00001211628,0.9751995,0.01460599,0.001396422,0.001829191,0.00002707106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2527452,0.001266012,0.7413467,0.000145168,0.00003121047,0.000154893,0.0002098213,0.001646701,0.002454328],"genre_scores_gemma":[0.880326,0.0002602234,0.1175947,0.0000345571,0.000008413304,0.00009924982,0.0001899298,0.0003481421,0.001138731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002994809,"threshold_uncertainty_score":0.008105159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139392377990762,"score_gpt":0.3460630844851137,"score_spread":0.3246691607052061,"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."}}