{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003050972,0.001575865,0.0009622117,0.007567652,0.001133257,0.002075415,0.002019302,0.001715307,0.003819783],"category_scores_gemma":[0.005198982,0.0004743896,0.001438155,0.002041923,0.0006030342,0.00304525,0.001854844,0.001814809,0.002254745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001523017,"about_ca_system_score_gemma":0.003855637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008516897,"about_ca_topic_score_gemma":0.003102828,"domain_scores_codex":[0.9982842,0.0004641772,0.0004217359,0.0001852751,0.0005442263,0.000100372],"domain_scores_gemma":[0.9980671,0.0003870389,0.0003578314,0.00008683869,0.000851561,0.000249652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000229351,0.0008358867,0.07565626,0.002003729,0.0001734231,0.005783274,0.0008275598,0.007797583,0.004849019,0.005146055,0.06529845,0.8313993],"study_design_scores_gemma":[0.000806493,0.00229285,0.1815705,0.01177637,0.0008207574,0.119969,0.008392452,0.08504666,0.01020509,0.04921069,0.529318,0.0005910972],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1252918,0.05938914,0.6228885,0.08147918,0.003918743,0.01877428,0.003689012,0.007105239,0.07746411],"genre_scores_gemma":[0.08796484,0.01757592,0.8808078,0.002478104,0.001002036,0.002572438,0.003299631,0.0002279105,0.004071351],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007567652,"threshold_uncertainty_score":0.01613528,"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."}}