{"id":"W4400947091","doi":"10.1371/journal.pone.0307531","title":"Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review","year":2024,"lang":"en","type":"review","venue":"PLoS ONE","topic":"Head and Neck Cancer Studies","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; Western University; Institute for Clinical Evaluative Sciences; University of Toronto; SickKids Foundation; Public Health Ontario; Hospital for Sick Children; Princess Margaret Cancer Centre","funders":"World Health Organization","keywords":"Head and neck cancer; Medicine; MEDLINE; Head and neck; Cancer; Internal medicine; Surgery; Biology","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":[0.01436159,0.001320555,0.006923348,0.0072464,0.0004140336,0.002522208,0.001814528,0.001793979,0.002385068],"category_scores_gemma":[0.09749702,0.0007756944,0.008368851,0.007417467,0.0009115009,0.002481866,0.001110783,0.001116736,0.0002135473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002589001,"about_ca_system_score_gemma":0.01008873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006093691,"about_ca_topic_score_gemma":0.01563043,"domain_scores_codex":[0.9867418,0.0058286,0.004351266,0.001079813,0.00176924,0.0002291389],"domain_scores_gemma":[0.9020693,0.08385435,0.009853212,0.0009852872,0.00295408,0.0002836504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001831468,0.00002207766,0.002993936,0.9379701,0.01365229,0.00005415208,0.0001762974,0.000339087,0.00006226592,0.0003008849,0.001019841,0.04322575],"study_design_scores_gemma":[0.0002251057,0.000260885,0.006637735,0.9162766,0.06693188,0.000220412,0.0002204486,0.0004152768,0.0001263806,0.0005824813,0.008057629,0.0000452459],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002166945,0.9958267,0.0004423408,0.000354393,0.00008566617,0.0002718555,0.0006701727,0.00000993373,0.0001720397],"genre_scores_gemma":[0.04363796,0.9518496,0.002015797,0.0007001674,0.0001586889,0.0009010357,0.000656489,0.000008424683,0.00007173209],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01436159,"threshold_uncertainty_score":0.07595223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2705257424731739,"score_gpt":0.41660904058864,"score_spread":0.1460832981154661,"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."}}