{"id":"W3043051650","doi":"10.1002/jrsm.1435","title":"Creating effective interrupted time series graphs: Review and recommendations","year":2020,"lang":"en","type":"review","venue":"Research Synthesis Methods","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"National Health and Medical Research Council; Canadian Institutes of Health Research; Monash University; Medical Research Council; Australian Government","keywords":"Computer science; Standardization; Visualization; Data mining; Data visualization; Graph; Software; Data extraction; Graph drawing; Information retrieval; Time series; Data science; Machine learning; Theoretical computer science; MEDLINE","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.16621,0.003388583,0.006241706,0.02743602,0.001429084,0.008814646,0.009167235,0.007840729,0.02955974],"category_scores_gemma":[0.588689,0.003355698,0.01401494,0.02149429,0.002759712,0.01523994,0.005237664,0.007655326,0.009064494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007673576,"about_ca_system_score_gemma":0.03317931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01363482,"about_ca_topic_score_gemma":0.02606188,"domain_scores_codex":[0.8495889,0.09270365,0.03642653,0.003990498,0.01583174,0.001458617],"domain_scores_gemma":[0.3905879,0.4313941,0.05100811,0.01876894,0.1045526,0.003688343],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003919131,0.00007661374,0.001045248,0.4231082,0.00267513,0.000227614,0.001853606,0.001208829,0.0004147949,0.008108456,0.2488465,0.312043],"study_design_scores_gemma":[0.0005578754,0.00009604017,0.001401443,0.6761913,0.004695759,0.0001633667,0.0008946139,0.001128153,0.0004949572,0.01459381,0.2995755,0.0002071911],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002205596,0.5241802,0.09560449,0.2731526,0.03699468,0.02622403,0.02298416,0.005256529,0.01339779],"genre_scores_gemma":[0.01983478,0.4818342,0.3700048,0.0588085,0.006293892,0.04663742,0.008531048,0.001252042,0.006803224],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8337899,"threshold_uncertainty_score":0.8790131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.214636014942542,"score_gpt":0.5644807030011398,"score_spread":0.3498446880585978,"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."}}