{"id":"W4390532249","doi":"10.1021/acs.jproteome.3c00756","title":"Liquid Biopsy Proteomics in Ophthalmology","year":2024,"lang":"en","type":"review","venue":"Journal of Proteome Research","topic":"Retinal Diseases and Treatments","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; VitreoRetinal Surgery Foundation; National Eye Institute; National Institutes of Health; Research to Prevent Blindness; Translational Research and Applied Medicine, School of Medicine, Stanford University","keywords":"Proteomics; Prognostics; Liquid biopsy; Sampling (signal processing); Biopsy; Computational biology; Pathology; Computer science; Medicine; Bioinformatics; Biology; Data mining; Internal medicine; Computer vision","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002565337,0.0003467289,0.002198343,0.002391849,0.00004542022,0.00006765332,0.0003887172,0.0004489042,0.0002145169],"category_scores_gemma":[0.0006535096,0.0002048282,0.0009831098,0.001428868,0.0001673608,0.00007665667,0.0002251114,0.00313856,0.0004697574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008902972,"about_ca_system_score_gemma":0.00322549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002391607,"about_ca_topic_score_gemma":2.71011e-7,"domain_scores_codex":[0.9958886,0.0006595566,0.00129159,0.0003626107,0.001168828,0.000628822],"domain_scores_gemma":[0.998123,0.0001657298,0.000382752,0.0003893691,0.0005163042,0.0004228678],"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":[0.004963346,0.003486163,0.0003017629,0.3296396,0.003685307,0.3134724,0.0001595381,3.987079e-7,0.0002246969,0.0003135776,0.002626702,0.3411266],"study_design_scores_gemma":[0.001742512,0.011973,0.0001691367,0.1858971,0.002387579,0.08408916,0.00005713403,0.000004472302,0.00002159574,0.0009219119,0.712325,0.0004113046],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003089043,0.9918661,0.000001834089,0.0003164183,0.0001948694,0.003247427,0.00002231677,0.000007022141,0.001255014],"genre_scores_gemma":[0.0002256321,0.9955691,0.001056704,0.000003000328,0.0006297957,0.0003094068,0.00001355429,0.00009084713,0.002101992],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7096984,"threshold_uncertainty_score":0.9991612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3184323325162389,"score_gpt":0.5691055028901195,"score_spread":0.2506731703738806,"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."}}