{"id":"W2909577051","doi":"10.2196/11342","title":"Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings","year":2019,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Dashboard; Usability; Dissemination; Context (archaeology); Computer science; Population; Data science; Task (project management); Data collection; Knowledge management; Medicine; Engineering; Geography; Environmental health; Human–computer interaction","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2709105,0.002791557,0.002789264,0.01485203,0.003064052,0.01898696,0.007969003,0.004707505,0.006279224],"category_scores_gemma":[0.3493816,0.002582647,0.005755546,0.009089391,0.003878044,0.01707012,0.01839708,0.007884028,0.002360167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005581992,"about_ca_system_score_gemma":0.02024639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004577602,"about_ca_topic_score_gemma":0.006931106,"domain_scores_codex":[0.8378259,0.1061159,0.02660461,0.009395045,0.017251,0.002807438],"domain_scores_gemma":[0.5522581,0.3039135,0.02630921,0.05764253,0.04260699,0.01726952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001615949,0.001242238,0.01938257,0.01203717,0.001306158,0.001357444,0.02393085,0.02963409,0.007526912,0.05879034,0.07705086,0.7661254],"study_design_scores_gemma":[0.001961041,0.002353748,0.01455166,0.02204749,0.0009641655,0.001484751,0.009439399,0.144459,0.01249051,0.2119907,0.5769273,0.001330269],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01459857,0.002458154,0.890967,0.02952581,0.0009814098,0.009851933,0.004524219,0.03986809,0.007224951],"genre_scores_gemma":[0.03524881,0.0008975276,0.9521883,0.001645871,0.0002241259,0.004990377,0.003097658,0.001059593,0.0006478106],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2709105,"threshold_uncertainty_score":0.8990971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3584710525886581,"score_gpt":0.599659220947585,"score_spread":0.2411881683589268,"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."}}