{"id":"W4416430908","doi":"10.12688/f1000research.169999.1","title":"Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE","year":2025,"lang":"en","type":"article","venue":"F1000Research","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Ottawa Hospital","funders":"","keywords":"Process (computing); Pipeline (software); Health care; Plan (archaeology); Data governance; Key (lock); Data integration; Corporate governance; Knowledge translation","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01487562,0.0001542751,0.0002458695,0.0007936984,0.003312405,0.0001854643,0.001303072,0.0000799328,0.0005078568],"category_scores_gemma":[0.004467247,0.0001374177,0.00001328767,0.002462453,0.0002407719,0.001303017,0.002137712,0.0005376677,0.0002590711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003909759,"about_ca_system_score_gemma":0.003439394,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01788582,"about_ca_topic_score_gemma":0.01646326,"domain_scores_codex":[0.9939576,0.002166232,0.0009866535,0.0008473644,0.0009118191,0.001130294],"domain_scores_gemma":[0.9938017,0.004200717,0.0001786301,0.0007945693,0.0006108107,0.0004135637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001662027,0.00002825012,0.3354364,0.001861706,0.00002320888,0.000004634944,0.05522861,0.000005503901,0.02159304,0.07597701,0.2692305,0.240445],"study_design_scores_gemma":[0.003532361,0.001486347,0.5354549,0.004601537,0.00003661535,0.000005387455,0.2184578,0.01164476,0.004725672,0.03110597,0.1879825,0.0009660856],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6264119,0.0002241478,0.01066717,0.3553493,0.0006308505,0.004585337,0.0002023292,0.0001738566,0.001755088],"genre_scores_gemma":[0.9633485,0.0002788945,0.006725398,0.02820924,0.0001138286,0.0005917011,0.00006602823,0.00001563197,0.0006507472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3369366,"threshold_uncertainty_score":0.9979851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9234416535612986,"score_gpt":0.8326399975545937,"score_spread":0.09080165600670487,"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."}}