{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":3,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":3,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"4f1a74221753","filters":{"venue":"Tuberculosis Lung Diseases HIV Infection"}},"results":[{"id":"W4381739476","doi":"10.30978/tb2023-2-28","title":"Ways of transformation of typical X-ray signs of community acquired pneumonia of viral etiology (COVID-19) according to radiomics data","year":2023,"lang":"en","type":"article","venue":"Tuberculosis Lung Diseases HIV Infection","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Pneumonia; Viral pneumonia; Medicine; Radiology; Coronavirus disease 2019 (COVID-19); Parenchyma; Lung; Pathological; Radiomics; Lung cancer; Pathology; Disease; Internal medicine; Infectious disease (medical specialty)","authors":[{"name":"М.І. Lynnyk","is_ca":false},{"name":"V. І. Іgnatieva","is_ca":false},{"name":"Г. Л. Гуменюк","is_ca":false},{"name":"О.К. Yakovenko","is_ca":false},{"name":"V.А. Svyatnenko","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06276501737141756,"gpt":0.3579258013577949,"spread":0.2951607839863774,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004049394,0.0002316084,0.0001689537,0.001750834,0.0002291398,0.0005206024,0.000184008,0.000245337,0.001727691],"category_scores_gemma":[0.002460649,0.0001579535,0.000312362,0.000689723,0.0004458936,0.0003476705,0.000436547,0.0002148877,0.0003483121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001722704,"about_ca_system_score_gemma":0.0001315596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006146966,"about_ca_topic_score_gemma":0.0004871099,"domain_scores_codex":[0.9994379,0.000146949,0.00007757972,0.0001174663,0.0001450064,0.00007517994],"domain_scores_gemma":[0.9988943,0.000253037,0.0004280702,0.0001090537,0.0001940166,0.0001215519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002322566,0.00001255372,0.9886708,0.00001953358,0.00003265162,0.0007857081,0.0001716775,0.0001132557,0.004100108,0.00006519214,0.00008431053,0.005711873],"study_design_scores_gemma":[0.000005740162,0.000111411,0.9919413,0.00000750436,0.00002222232,0.00550561,0.000230831,0.000362902,0.001254229,0.00006386254,0.0004874187,0.000006938546],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971514,0.0004546159,0.0008325812,0.00002519725,0.000005187585,0.00002068483,0.0002205603,0.0000261661,0.001263448],"genre_scores_gemma":[0.9991977,0.0001165559,0.0003426257,0.000006413316,0.000006120884,0.000009823052,0.0001986831,0.000003912463,0.0001182435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001750834,"threshold_uncertainty_score":0.005779743,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4283380731","doi":"10.30978/tb-2022-2-36","title":"Possibilities of radiomics in processing data of CT scan of the chest organs in diagnosis of pulmonary tuberculosis","year":2022,"lang":"en","type":"article","venue":"Tuberculosis Lung Diseases HIV Infection","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"DICOM; Grayscale; Software; Histogram; Image processing; Computer science; Artificial intelligence; Medical imaging; Sørensen–Dice coefficient; Radiology; Pixel; Segmentation; Medicine; Computer vision; Image segmentation; Image (mathematics)","authors":[{"name":"М.І. Lynnyk","is_ca":false},{"name":"І. В. Ліскіна","is_ca":false},{"name":"І.А. Kalabukha","is_ca":false},{"name":"V. І. Іgnatieva","is_ca":false},{"name":"Olga Tarasenko","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01225504274412003,"gpt":0.2821881552623861,"spread":0.269933112518266,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003611211,0.0007032339,0.0005319146,0.004340935,0.0003263764,0.001460243,0.0007570844,0.0007747089,0.002605918],"category_scores_gemma":[0.008139047,0.0003827268,0.0006910112,0.002435618,0.0009661271,0.001363488,0.001054563,0.0007242444,0.0009597154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002869991,"about_ca_system_score_gemma":0.0004699415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003910157,"about_ca_topic_score_gemma":0.0004636119,"domain_scores_codex":[0.997235,0.001508176,0.0002456373,0.0003231321,0.0005657672,0.0001223538],"domain_scores_gemma":[0.9951499,0.003018334,0.0002333081,0.0006211791,0.0008435901,0.0001336574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001499103,0.0001762474,0.01484164,0.001103313,0.0001551612,0.002134969,0.00122655,0.005843809,0.1084255,0.01021483,0.005173586,0.8492053],"study_design_scores_gemma":[0.0004032786,0.002612302,0.09199099,0.001592185,0.001859349,0.02795277,0.002873141,0.2248711,0.401756,0.07651992,0.1668004,0.0007685935],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0622516,0.006699149,0.9158744,0.001332675,0.0003675167,0.0003005145,0.0004121003,0.002747897,0.01001421],"genre_scores_gemma":[0.3542801,0.004098605,0.6371887,0.0005322113,0.0005891244,0.0003221347,0.0004241198,0.0005211767,0.002043842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004340935,"threshold_uncertainty_score":0.0190981,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392369445","doi":"10.30978/tb2024-1-86","title":"Differential Diagnostics of the Disappearing Lung Syndrome in Lymphangioleiomyomatosis and COVID-19 Pneumonia Using Digital Software Processing of Computer Tomography Data (Clinical Cases)","year":2024,"lang":"en","type":"article","venue":"Tuberculosis Lung Diseases HIV Infection","topic":"Medical Imaging and Pathology Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Lymphangioleiomyomatosis; Pneumonia; Medicine; Software; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Differential diagnosis; Tomography; Computed tomography; 2019-20 coronavirus outbreak; Computer science; Lung; Radiology; Pathology; Internal medicine; Disease; Infectious disease (medical specialty); Operating system","authors":[{"name":"М.І. Lynnyk","is_ca":false},{"name":"І. В. Ліскіна","is_ca":false},{"name":"V. І. Іgnatieva","is_ca":false},{"name":"Г. Л. Гуменюк","is_ca":false},{"name":"V.А. Svyatnenko","is_ca":false},{"name":"Oksana Chobotar","is_ca":false},{"name":"О.К. Yakovenko","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0411855077692938,"gpt":0.3562828700782526,"spread":0.3150973623089588,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005062968,0.0003911044,0.0002252814,0.002740337,0.0002790164,0.0005032797,0.0002457931,0.0004160469,0.001616769],"category_scores_gemma":[0.001625692,0.0002560425,0.0002694871,0.0008799264,0.0005193229,0.0004999986,0.0005089762,0.0002400059,0.0002008291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002244829,"about_ca_system_score_gemma":0.0003050978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001243944,"about_ca_topic_score_gemma":0.001601431,"domain_scores_codex":[0.9995752,0.00009457317,0.00008830864,0.00007144158,0.00009197914,0.00007855615],"domain_scores_gemma":[0.9994817,0.0001726058,0.0001159832,0.00003873432,0.00009155538,0.00009953058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"case_report","study_design_scores_codex":[0.0004457482,0.00008331434,0.9374658,0.00009220166,0.00002903363,0.02014564,0.0003610608,0.0001782461,0.0211712,0.0001452234,0.0002384829,0.01964404],"study_design_scores_gemma":[0.0000308518,0.0004249851,0.8937702,0.0000509694,0.00006298697,0.09189684,0.001095201,0.002651054,0.007913928,0.0002121029,0.001867226,0.00002369701],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957017,0.0004908045,0.001825683,0.00006089422,0.00001083924,0.00006985242,0.0001479046,0.00002623723,0.001666174],"genre_scores_gemma":[0.9959288,0.0003016412,0.003266991,0.00002576345,0.00001829829,0.00003000758,0.0001781561,0.000004822879,0.0002455702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002740337,"threshold_uncertainty_score":0.005408645,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}