{"id":"W2079619641","doi":"10.1097/prs.0b013e3181da872e","title":"Nasal Reconstruction after Malignant Tumor Resection: An Algorithm for Treatment","year":2010,"lang":"en","type":"article","venue":"Plastic & Reconstructive Surgery","topic":"Reconstructive Facial Surgery Techniques","field":"Medicine","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Resection; Medicine; Algorithm; Computer science; Surgery","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006271785,0.0006188119,0.001207033,0.000854475,0.0002989208,0.00009570104,0.0001024616,0.0003628097,0.001199397],"category_scores_gemma":[0.001039803,0.0005514524,0.0006508459,0.0004397063,0.0008324849,0.0006917791,0.00003404485,0.0004108714,0.00004628894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000393755,"about_ca_system_score_gemma":0.0007954592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001065294,"about_ca_topic_score_gemma":0.0001748434,"domain_scores_codex":[0.9965871,0.0001498416,0.0009614609,0.001129032,0.0003597349,0.0008128134],"domain_scores_gemma":[0.9954589,0.002254583,0.0004062593,0.0006368583,0.0006833093,0.0005600747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001538136,0.0002456855,0.3268863,0.00004821706,0.0002615063,0.0001297133,0.00005073108,2.498509e-7,0.009625455,0.000148843,0.0001244753,0.6609406],"study_design_scores_gemma":[0.004719731,0.004075821,0.6917406,0.0009286341,0.001033784,0.03232712,0.001172988,0.003976197,0.234359,0.008062983,0.01472308,0.002879994],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782414,0.00005535498,0.009039143,0.00003253841,0.009412112,0.001363801,0.0004217043,0.00047014,0.0009638253],"genre_scores_gemma":[0.9346842,0.00004968596,0.06093166,0.00008803519,0.002110261,0.0016469,0.0001434413,0.0001403775,0.0002055098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6580607,"threshold_uncertainty_score":0.9997137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197980344925205,"score_gpt":0.2745516158715627,"score_spread":0.2547535813790422,"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."}}