{"id":"W4367592794","doi":"10.2196/43963","title":"Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Peripheral Artery Disease Management","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute for Health and Care Research","keywords":"Context (archaeology); Workflow; Stakeholder; Medicine; Thematic analysis; Action plan; Knowledge translation; Knowledge management; Computer science; Process management; Machine learning; Qualitative research; Engineering; Public relations","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":[0.02827111,0.0005179333,0.0006986121,0.001630165,0.007153131,0.003573218,0.001974525,0.001758427,0.00289039],"category_scores_gemma":[0.04275152,0.000671147,0.0004161127,0.001212536,0.006232862,0.004068126,0.005621609,0.002697229,0.0004312677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007010468,"about_ca_system_score_gemma":0.009955177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007266364,"about_ca_topic_score_gemma":0.009563603,"domain_scores_codex":[0.9800894,0.01545048,0.0005769766,0.0008761521,0.001390403,0.001616692],"domain_scores_gemma":[0.9500679,0.04053388,0.002207278,0.000747247,0.00385794,0.002585619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002921901,0.00006930345,0.003700374,0.0002288673,0.000004356048,0.0006639241,0.9878588,0.00005710677,0.0007223521,0.001110786,0.000439781,0.005115131],"study_design_scores_gemma":[0.000004268159,0.0000663696,0.001448459,0.0002574502,0.000003750349,0.0002136121,0.9924549,0.0002120126,0.0003641633,0.000350798,0.004612932,0.00001130729],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845324,0.0006432477,0.00710781,0.00277297,0.00005892145,0.0006470284,0.0001819203,0.00002052852,0.004035271],"genre_scores_gemma":[0.9909671,0.0008123367,0.00394755,0.001160618,0.00001395788,0.0006636463,0.00008062699,0.00003331685,0.002320843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02827111,"threshold_uncertainty_score":0.1495137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1268799325513171,"score_gpt":0.4749965112034376,"score_spread":0.3481165786521204,"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."}}