{"id":"W4400723454","doi":"10.1016/j.arbres.2024.07.007","title":"Protein Biomarkers in Lung Cancer Screening: Technical Considerations and Feasibility Assessment","year":2024,"lang":"es","type":"review","venue":"Archivos de Bronconeumología","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Public Health Ontario; University of Toronto","funders":"National Cancer Institute; Instituto de Salud Carlos III; Ministerio de Universidades; Centro de Investigación Biomédica en Red de Cáncer; Fundación Científica Asociación Española Contra el Cáncer; World Health Organization","keywords":"Lung cancer; Medicine; Context (archaeology); Lung cancer screening; Intensive care medicine; Cancer; Prognostics; Biomarker; Stage (stratigraphy); Biomarker discovery; Bioinformatics; Oncology; Internal medicine; Biology; Computer science; Proteomics","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":[0.002809963,0.000952262,0.001496369,0.002538142,0.0003124627,0.002000994,0.001120525,0.002125232,0.002199451],"category_scores_gemma":[0.003365856,0.0004974026,0.000886382,0.002173426,0.001024598,0.002409816,0.000830479,0.002304963,0.002175867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008683963,"about_ca_system_score_gemma":0.001732549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139057,"about_ca_topic_score_gemma":0.001146032,"domain_scores_codex":[0.999099,0.0002403321,0.00009957553,0.0001637965,0.0003324887,0.00006473345],"domain_scores_gemma":[0.9974062,0.001809672,0.0001540749,0.00006924212,0.0004917487,0.00006902626],"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":[0.00009721883,0.00007580093,0.0006631661,0.01964777,0.0001409608,0.0002849379,0.00008337125,0.000772801,0.004177557,0.01055513,0.01375917,0.949742],"study_design_scores_gemma":[0.00002128276,0.0002731769,0.002208529,0.006132675,0.0002487371,0.002334271,0.0001255266,0.0007980188,0.00422618,0.006844169,0.9767271,0.00006026583],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002386564,0.9969196,0.0008318607,0.0006787888,0.0001911717,0.000008141855,0.00002529242,0.0000105728,0.001095834],"genre_scores_gemma":[0.002066546,0.9953998,0.001321311,0.0003925585,0.0002380604,0.00001936085,0.00004422732,0.00000454052,0.000513631],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002809963,"threshold_uncertainty_score":0.01486063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02799291633436322,"score_gpt":0.3842618789945391,"score_spread":0.3562689626601758,"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."}}