{"id":"W4408599608","doi":"10.1117/12.3042275","title":"Spatial-frequency 3D fluorescence for surgical guidance: margin thickness quantification","year":2025,"lang":"en","type":"article","venue":"","topic":"Intraocular Surgery and Lenses","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Margin (machine learning); Computer science; Biomedical engineering; Materials science; Medicine; Machine learning","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":[0.0004775952,0.0001124177,0.0002309196,0.00009173797,0.000106661,0.00002012007,0.00007923984,0.0001214904,0.0004191739],"category_scores_gemma":[0.0003466299,0.0000892517,0.0001111718,0.000204518,0.00007404683,0.00005837161,0.00001390877,0.0001147494,0.00005000022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003526715,"about_ca_system_score_gemma":0.0001798104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001528385,"about_ca_topic_score_gemma":0.00002018267,"domain_scores_codex":[0.9990184,0.00004923078,0.0002912694,0.0002712834,0.0001514546,0.0002183034],"domain_scores_gemma":[0.9990821,0.0002443312,0.00004379789,0.0003369277,0.0002410637,0.00005180992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001981826,0.0008329151,0.1095413,0.001722735,0.00024265,0.0002338643,0.0002694462,0.000008762921,0.06332058,0.5474747,0.03750226,0.236869],"study_design_scores_gemma":[0.00771831,0.0004403785,0.1777998,0.001686838,0.0004962768,0.000225816,0.000305272,0.01610421,0.2570129,0.007197214,0.5300594,0.0009536311],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3813518,0.002555644,0.4089265,0.03438555,0.0040201,0.003839378,0.00004611055,0.0007564387,0.1641185],"genre_scores_gemma":[0.9733455,0.00007261235,0.00965405,0.0006700159,0.0001844268,0.0001060011,0.00007422842,0.00001188024,0.01588136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5919936,"threshold_uncertainty_score":0.4589661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036430089568153,"score_gpt":0.3054900541015521,"score_spread":0.2851257532058706,"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."}}