{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001288574,0.001106314,0.001500248,0.00314835,0.0003659781,0.001994478,0.0008517482,0.001924646,0.002648571],"category_scores_gemma":[0.001170601,0.0004132363,0.0007497569,0.002988546,0.0008730973,0.002431477,0.001322235,0.002340621,0.002754062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000762872,"about_ca_system_score_gemma":0.001268906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00102228,"about_ca_topic_score_gemma":0.001234203,"domain_scores_codex":[0.9994819,0.0001006731,0.00005793113,0.00008888862,0.0002251306,0.00004551917],"domain_scores_gemma":[0.999278,0.0003758281,0.00008273724,0.0000297451,0.0001826088,0.00005104478],"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":[0.00005586083,0.00006569034,0.0002524487,0.01353325,0.000111915,0.0002481393,0.00007172939,0.0003322915,0.004347534,0.007201401,0.02428485,0.9494948],"study_design_scores_gemma":[0.00001032588,0.00009341836,0.001023812,0.003515671,0.00009087186,0.001870844,0.00009609723,0.0002361248,0.001760893,0.005330262,0.9859309,0.00004074698],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001298779,0.9979668,0.0004832453,0.0002739708,0.0002369433,0.000005688338,0.00001761391,0.0000168315,0.000869111],"genre_scores_gemma":[0.000992288,0.9971781,0.0006745201,0.0002941723,0.0002261927,0.000009070974,0.00004155389,0.000003869303,0.0005802736],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00314835,"threshold_uncertainty_score":0.00886035,"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."}}