{"id":"W4402691282","doi":"10.2196/64675","title":"Peer Review of “A Hybrid Pipeline for Covid-19 Screening Incorporating Lungs Segmentation and Wavelet Based Preprocessing of Chest X-Rays (Preprint)”","year":2024,"lang":"en","type":"article","venue":"JMIRx Med","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Preprint; Coronavirus disease 2019 (COVID-19); Preprocessor; Pipeline (software); Segmentation; 2019-20 coronavirus outbreak; Computer science; Wavelet; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Artificial intelligence; Medicine; Virology; Internal medicine; Operating system; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002439726,0.0001767673,0.0005203081,0.0001991239,0.00006179175,0.00002984276,0.00008381352,0.00004922182,0.00007005518],"category_scores_gemma":[0.009693641,0.0001630001,0.0001220162,0.0003574835,0.00009862987,0.0001320223,0.00006226249,0.0001518058,0.000001459993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001455239,"about_ca_system_score_gemma":0.0006537802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000774955,"about_ca_topic_score_gemma":0.000005324527,"domain_scores_codex":[0.9979438,0.00007074138,0.0007302977,0.0004757064,0.0006109536,0.0001685181],"domain_scores_gemma":[0.9973894,0.001044926,0.0003648703,0.0003260447,0.0007082631,0.0001665103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006156933,0.0004770994,0.006545749,0.4508165,0.0002345131,0.00008904712,0.002316174,0.0004345316,0.1598388,0.0001242153,0.1610429,0.2174647],"study_design_scores_gemma":[0.00711729,0.0006002483,0.002510013,0.1112727,0.00161278,0.0001061387,0.0006231579,0.3649475,0.3127488,0.0005012134,0.1972397,0.0007204554],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04659432,0.0155521,0.5478972,0.3833309,0.00023734,0.005625519,0.0001861083,0.0003874608,0.0001889773],"genre_scores_gemma":[0.8416825,0.0005399647,0.125092,0.0298518,0.0002501639,0.0005322641,0.0006043972,0.000103465,0.001343407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7950882,"threshold_uncertainty_score":0.9986481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06331668958435178,"score_gpt":0.3926185420742904,"score_spread":0.3293018524899386,"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."}}