{"id":"W4378627654","doi":"10.28942/mgpam.v10i1.93","title":"Оптимизация лечения маточного бесплодия на основе использования богатого тромбоцитами фибрина (I-PRF)","year":2023,"lang":"ru","type":"article","venue":"Actual Questions of Modern Gynecology and Perinatology","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cytodiagnostics (Canada)","funders":"","keywords":"Materials science","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":[0.00247093,0.0005882386,0.0007603535,0.002333985,0.001495677,0.004979587,0.0009568116,0.001258006,0.0170294],"category_scores_gemma":[0.005042784,0.0004873189,0.0007538756,0.001858512,0.00191055,0.00187097,0.001764697,0.001661557,0.004070004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00187653,"about_ca_system_score_gemma":0.003547326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002624744,"about_ca_topic_score_gemma":0.002474169,"domain_scores_codex":[0.9971577,0.0006352845,0.0001894694,0.0003985103,0.001334589,0.0002844606],"domain_scores_gemma":[0.9976427,0.0008073629,0.0004890579,0.0002699115,0.0006238466,0.0001670866],"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":[0.0004514308,0.0003326487,0.01718219,0.003582733,0.0002277252,0.001832479,0.002667192,0.003567488,0.07216094,0.2634334,0.01808282,0.616479],"study_design_scores_gemma":[0.0001435306,0.0008881125,0.02057518,0.001496907,0.000363639,0.008036715,0.00340921,0.005443395,0.08760884,0.1045008,0.7672423,0.0002914886],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1298129,0.1391214,0.2365507,0.01360335,0.003223803,0.001195065,0.002168743,0.0008949831,0.4734292],"genre_scores_gemma":[0.8019968,0.05722961,0.08684976,0.001792215,0.00114579,0.001031552,0.0007169473,0.0003880302,0.04884934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0170294,"threshold_uncertainty_score":0.05696905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0146744743577565,"score_gpt":0.2529649944612649,"score_spread":0.2382905201035083,"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."}}