{"id":"W4416385253","doi":"10.1021/acs.oprd.5c00326","title":"Simplifying “SiFA”: A High-Yielding, Automated Protocol for the One-Step Radiosynthesis of the Neuroendocrine Tumor Imaging Agent [ <sup>18</sup> F]SiTATE <i>via</i> a Merging of “Silicon-Fluoride Acceptor” (SiFA) and “Nonanhydrous, Minimally Basic” (NAMB) Chemistries","year":2025,"lang":"en","type":"article","venue":"Organic Process Research & Development","topic":"Radiopharmaceutical Chemistry and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; McMaster University","funders":"Canadian Institutes of Health Research; Saskatchewan Health Research Foundation; Canada Research Chairs; Neuroendocrine Tumor Research Foundation; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; McMaster University; Education and Research Foundation for Nuclear Medicine and Molecular Imaging","keywords":"Radiosynthesis; Protocol (science); Neuroendocrine tumors; Molecular imaging","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008941569,0.0003088029,0.0005119668,0.0001206477,0.0006588812,0.00006954973,0.0006402482,0.00004095307,0.0001417162],"category_scores_gemma":[0.001451368,0.0002144969,0.0000981612,0.001119845,0.0004904081,0.0001048919,0.0004541724,0.0005053122,0.000001890777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002413677,"about_ca_system_score_gemma":0.001623732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002100192,"about_ca_topic_score_gemma":0.000002297941,"domain_scores_codex":[0.9969677,0.00008074076,0.000820769,0.0006036386,0.0008258321,0.0007012635],"domain_scores_gemma":[0.9974214,0.001016013,0.0002386029,0.0005519661,0.0005795702,0.000192467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008761819,0.0005180013,0.00379307,0.008173958,0.0004393877,0.00002129417,0.000807158,0.00005404374,0.963441,0.00008207725,0.002144434,0.01964943],"study_design_scores_gemma":[0.00194347,0.00003771892,0.001738993,0.000894757,0.0001159052,0.00006436234,0.0005324751,0.009531963,0.9747126,0.00009345739,0.01015497,0.0001793937],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9088525,0.0004976302,0.001435354,0.01464389,0.00003800031,0.07313076,0.00004449674,0.0002991984,0.001058213],"genre_scores_gemma":[0.9492375,0.00004381303,0.001351883,0.0002155501,0.00005440129,0.04869112,0.000008152744,0.00004723289,0.0003503975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.040385,"threshold_uncertainty_score":0.8746929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04471368039633308,"score_gpt":0.3838993482905736,"score_spread":0.3391856678942406,"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."}}