{"id":"W4391069593","doi":"10.1016/j.thromres.2024.01.009","title":"TRanEXamic acid to decrease Heavy Menstrual Bleeding in individuals anticoagulated for venous thromboembolism (T-REX HMB): Health care practitioner survey","year":2024,"lang":"en","type":"letter","venue":"Thrombosis Research","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nova Scotia Health Authority; McGill University; Jewish General Hospital; University of Ottawa; Ottawa Hospital; Dalhousie University; University Health Network; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Tranexamic acid; Medicine; Venous thrombosis; Thrombosis; Menstrual bleeding; Complication; Menstrual cycle; Venous thromboembolism; Antifibrinolytic; Anemia; Obstetrics; Surgery; Blood loss; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.006712184,0.001080578,0.003891706,0.003600573,0.0006725696,0.0005626806,0.0009026209,0.001411008,0.0006441175],"category_scores_gemma":[0.001775322,0.001046956,0.000497349,0.002850829,0.0003516791,0.0002681754,0.000623066,0.005092077,0.0005707205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002648025,"about_ca_system_score_gemma":0.003617076,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01408562,"about_ca_topic_score_gemma":0.002743531,"domain_scores_codex":[0.9877236,0.001629422,0.001658382,0.002386002,0.003248485,0.003354128],"domain_scores_gemma":[0.9943331,0.001430342,0.0002976719,0.001552349,0.001202132,0.001184397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001255581,0.001279623,0.0004863623,0.006745748,0.001363855,0.0003008823,0.007453142,0.00002583338,0.0007178791,0.00008823867,0.9785351,0.002877815],"study_design_scores_gemma":[0.00568501,0.008387492,0.2069221,0.01032179,0.001389737,0.0001004043,0.003678679,0.000154092,0.002121002,0.0004798239,0.7583116,0.002448242],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2305938,0.005865525,0.00007162396,0.7357941,0.001999516,0.01912772,0.003432359,0.000499326,0.00261602],"genre_scores_gemma":[0.5907711,0.005265627,0.001385287,0.3774591,0.005099354,0.003912974,0.01437132,0.001024944,0.000710206],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.3601773,"threshold_uncertainty_score":0.9998854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1452242909274184,"score_gpt":0.4421443120771975,"score_spread":0.296920021149779,"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."}}