{"id":"W3202689561","doi":"10.1101/2021.09.21.21263906","title":"Connecting Artificial Intelligence and Primary Care Challenges: Findings from a Multi-Stakeholder Collaborative Consultation","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thames Valley Children's Centre; Middlesex London Health Unit; Toronto Rehabilitation Institute; Western University","funders":"Ontario Ministry of Health and Long-Term Care","keywords":"Knowledge management; Stakeholder; Profiling (computer programming); Interoperability; Stakeholder engagement; Process management; Computer science; Business; Public relations; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.06641794,0.0005943553,0.001195493,0.002633295,0.03991213,0.009007021,0.004012515,0.004953562,0.004044054],"category_scores_gemma":[0.1261731,0.0008967791,0.0007574705,0.003810124,0.01681732,0.003863321,0.02158763,0.005495299,0.0002877163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04535399,"about_ca_system_score_gemma":0.1000473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3401322,"about_ca_topic_score_gemma":0.4722603,"domain_scores_codex":[0.8646218,0.1091782,0.003178973,0.003757198,0.008309218,0.01095464],"domain_scores_gemma":[0.725967,0.2248015,0.006943886,0.004368646,0.01706524,0.02085373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004380487,0.00007983606,0.005231345,0.00027534,0.00001557491,0.001379719,0.9846919,0.0000724955,0.0001050701,0.0006415228,0.001370504,0.006092906],"study_design_scores_gemma":[0.00002036467,0.00005734615,0.004384488,0.000222088,0.00001262393,0.0001835469,0.9886442,0.0001608983,0.0000592736,0.000566108,0.005666557,0.00002237398],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9477899,0.001205566,0.002775995,0.02659804,0.0001470541,0.00166934,0.0002850231,0.00005068834,0.01947834],"genre_scores_gemma":[0.9939842,0.000420607,0.001284992,0.0027969,0.00002205169,0.000495104,0.00003943703,0.0000199584,0.0009368116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3401322,"threshold_uncertainty_score":0.6763048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3292985217329508,"score_gpt":0.4075234069141542,"score_spread":0.07822488518120346,"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."}}