{"id":"W4414675235","doi":"10.52058/2786-4952-2025-9(55)-1682-1697","title":"IRON DEFICIENCY ANAEMIA: REFLECTING SCIENTIFIC PROGRESS AND TRENDS THROUGH BIBLIOMETRIC ANALYSIS","year":2025,"lang":"uk","type":"article","venue":"Перспективи та інновації науки","topic":"Iron Metabolism and Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scientific progress; Iron deficiency; Bibliometrics; Trend analysis; Technical progress","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01266151,0.0008483652,0.003540281,0.1726941,0.001123679,0.006227748,0.001365757,0.001036226,0.00568661],"category_scores_gemma":[0.08194821,0.0004755076,0.003155188,0.2829718,0.0009206048,0.003713644,0.002802293,0.0009588927,0.001201437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003591821,"about_ca_system_score_gemma":0.008302133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01101956,"about_ca_topic_score_gemma":0.01268407,"domain_scores_codex":[0.9742526,0.00466381,0.0088128,0.00230451,0.009046856,0.0009194895],"domain_scores_gemma":[0.8906463,0.06894315,0.016929,0.002390161,0.01953832,0.001553124],"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":[0.0005207855,0.0001764661,0.3283456,0.1448664,0.00908004,0.0007297737,0.005086774,0.002581175,0.001166543,0.007601972,0.05638713,0.4434575],"study_design_scores_gemma":[0.0001985991,0.0002844446,0.6392212,0.03908019,0.01080835,0.001689319,0.01049737,0.007556961,0.001345925,0.008026848,0.2809033,0.0003875214],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2584008,0.4262587,0.009350444,0.01364801,0.001224391,0.002079081,0.2520873,0.001387722,0.03556349],"genre_scores_gemma":[0.6201226,0.2411217,0.02500507,0.001188078,0.00108517,0.003425764,0.1044554,0.0002632173,0.003333026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8273059,"threshold_uncertainty_score":0.06696129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03399909291556662,"score_gpt":0.3738769672539739,"score_spread":0.3398778743384073,"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."}}