{"id":"W4409248947","doi":"10.1111/trf.18234","title":"How do we leverage implementation science to support and accelerate uptake of clinical practice guidelines in transfusion medicine","year":2025,"lang":"en","type":"article","venue":"Transfusion","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Canadian Blood Services; Queen's University; Ottawa Hospital","funders":"Health Canada; University College London; Canadian Blood Services; Australian Government","keywords":"Guideline; Leverage (statistics); General partnership; Clinical Practice; Transfusion medicine; Medicine; Management science; Process management; Computer science; Nursing; Political science; Business; Engineering; Blood transfusion; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.01624695,0.0001852746,0.0005110659,0.0007948031,0.0006468816,0.00002594426,0.0003511809,0.0001380795,0.0004786407],"category_scores_gemma":[0.00580642,0.0001532798,0.00003906513,0.002406967,0.0002771041,0.0008803891,0.00009643791,0.0004565709,0.00001154502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001390355,"about_ca_system_score_gemma":0.002097474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003583205,"about_ca_topic_score_gemma":0.00537758,"domain_scores_codex":[0.9942141,0.0007629371,0.002769845,0.0006736097,0.0009137167,0.0006658243],"domain_scores_gemma":[0.995843,0.001769808,0.000376186,0.0003623078,0.001318354,0.0003303429],"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":[0.0005224915,0.00007559908,0.03771451,0.0005536709,0.000009383524,0.000006864394,0.03765328,0.000007907263,0.06509344,0.004781161,0.01569502,0.8378867],"study_design_scores_gemma":[0.008708271,0.001190777,0.2868988,0.001123692,0.00005811194,0.000004190083,0.04949081,0.0001847206,0.00260007,0.0006579852,0.6488053,0.0002773255],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6864367,0.00006662325,0.006876804,0.3005009,0.0009096853,0.002146855,0.00003440137,0.00003600249,0.002992074],"genre_scores_gemma":[0.9330887,0.01011627,0.01233466,0.04320033,0.0002363334,0.0001922952,0.000008705173,0.00002015912,0.0008025752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8376094,"threshold_uncertainty_score":0.6951247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6242818315396532,"score_gpt":0.718350421691282,"score_spread":0.09406859015162883,"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."}}