{"id":"W4386038848","doi":"10.2196/44732","title":"Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning–Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders","year":2023,"lang":"en","type":"article","venue":"JMIR Cardio","topic":"Clinical practice guidelines implementation","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Thematic analysis; Medicine; Implementation research; Intervention (counseling); Population; Family medicine; Health care; Medical education; Best practice; Qualitative research; Nursing; Psychological intervention","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0428175,0.0005917938,0.0008065884,0.001034229,0.003069032,0.002315788,0.0009494053,0.001764196,0.002489499],"category_scores_gemma":[0.1021579,0.001244159,0.001036259,0.0008804502,0.001939889,0.002443178,0.003556071,0.0023805,0.0003683588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0047227,"about_ca_system_score_gemma":0.01000878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006089643,"about_ca_topic_score_gemma":0.008600114,"domain_scores_codex":[0.9726579,0.01871237,0.001945039,0.001225643,0.002073624,0.003385395],"domain_scores_gemma":[0.9200587,0.05318718,0.009044271,0.002599799,0.01107161,0.004038377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.001563914,0.01293856,0.2735242,0.00104145,0.000118044,0.0008264626,0.6464442,0.0004453767,0.00362418,0.0004072286,0.0009925049,0.05807384],"study_design_scores_gemma":[0.0008061973,0.02755714,0.3481264,0.0008283366,0.0002194115,0.0004719841,0.6076449,0.002416499,0.004751031,0.0004208713,0.006539354,0.000217904],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981233,0.00003846632,0.0004901782,0.0002487101,0.00000708582,0.0006649356,0.00004205107,0.000007887612,0.0003773439],"genre_scores_gemma":[0.9947095,0.0001134894,0.002737298,0.0003522016,0.00001173116,0.001690046,0.00005908596,0.00001060449,0.0003159883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0428175,"threshold_uncertainty_score":0.2264432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09260024491923896,"score_gpt":0.4054005166659151,"score_spread":0.3128002717466762,"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."}}