{"id":"W2125843529","doi":"10.1118/1.1368127","title":"Inverse planning anatomy‐based dose optimization for HDR‐brachytherapy of the prostate using fast simulated annealing algorithm and dedicated objective function","year":2001,"lang":"en","type":"article","venue":"Medical Physics","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":268,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Hôtel-Dieu de Québec","funders":"National Cancer Institute","keywords":"Brachytherapy; Prostate; Radiation treatment planning; Urethra; Dosimetry; Computer science; Simulated annealing; Prostate brachytherapy; Prostate cancer; Algorithm; Medicine; Medical physics; Nuclear medicine; Radiology; Urology; Radiation therapy; Internal medicine; Cancer","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":[0.0005314127,0.0006262357,0.0005811722,0.0003737865,0.000419924,0.0005437813,0.0004851496,0.0006001196,0.001444397],"category_scores_gemma":[0.0009077793,0.0006524433,0.0009121964,0.0003189253,0.0004773785,0.0002890895,0.0004574319,0.0007633315,0.0004823336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000994637,"about_ca_system_score_gemma":0.001276189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002704146,"about_ca_topic_score_gemma":0.003826266,"domain_scores_codex":[0.999729,0.0001015038,0.00001139359,0.00003109249,0.0001076252,0.00001934384],"domain_scores_gemma":[0.9997256,0.0001544323,0.00003121792,0.00003194004,0.00004685172,0.000009960781],"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":[0.0000477816,0.00003234607,0.000244484,0.00009799025,0.00004399631,0.00004538667,0.0000986799,0.9144313,0.02198495,0.006799881,0.0009516123,0.05522164],"study_design_scores_gemma":[0.00001104026,0.00002678615,0.0002201439,0.000005027701,0.00001006556,0.00004479504,0.000004574029,0.9906659,0.005222162,0.001567545,0.002211876,0.00001005263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004236332,0.0000946236,0.9937592,0.00004458493,0.0000119342,0.0000321545,0.00001835977,0.0005390575,0.001263808],"genre_scores_gemma":[0.1231701,0.0001582595,0.8736003,0.00004781011,0.00001268251,0.0002332677,0.0001186078,0.0003539368,0.002304996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002704146,"threshold_uncertainty_score":0.007216632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02620324189709338,"score_gpt":0.3131541672329541,"score_spread":0.2869509253358607,"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."}}