{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1423385,0.001107157,0.0008695364,0.004480621,0.002492067,0.01755952,0.004515581,0.004006602,0.01320812],"category_scores_gemma":[0.1552949,0.001249787,0.002900603,0.003118117,0.01270394,0.01906808,0.0282982,0.008326489,0.004146118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007638991,"about_ca_system_score_gemma":0.03365603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001816292,"about_ca_topic_score_gemma":0.002789859,"domain_scores_codex":[0.8712565,0.09963432,0.006526797,0.006270722,0.01371765,0.002594018],"domain_scores_gemma":[0.7861827,0.1410984,0.006442615,0.04258555,0.01787017,0.005820546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002635425,0.0007794904,0.005164649,0.004675753,0.0002781342,0.0004022217,0.01394644,0.007136003,0.004276547,0.5326881,0.02142771,0.4089614],"study_design_scores_gemma":[0.0003154626,0.000699536,0.003235436,0.005533931,0.0001613256,0.0005531433,0.004880311,0.01432274,0.007992778,0.5202104,0.4419579,0.0001369038],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0141682,0.002009459,0.8456064,0.07041094,0.0009088363,0.003161599,0.0006297526,0.002589551,0.06051516],"genre_scores_gemma":[0.06031129,0.000873776,0.9266694,0.004334253,0.000133967,0.002184818,0.0006363464,0.0004295804,0.004426665],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1423385,"threshold_uncertainty_score":0.752767,"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."}}