{"id":"W3087379872","doi":"10.1017/s0266462320000628","title":"Real-world data for health technology assessment for reimbursement decisions in Asia: current landscape and a way forward","year":2020,"lang":"en","type":"article","venue":"International Journal of Technology Assessment in Health Care","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Hospital; St. Michael's Hospital; Canadian Centre for Applied Research in Cancer Control; Sunnybrook Health Science Centre","funders":"Japan Society for the Promotion of Science; National Evidence-based Healthcare Collaborating Agency; Department for International Development; Bill and Melinda Gates Foundation","keywords":"Reimbursement; Health technology; Consistency (knowledge bases); Context (archaeology); Business; Medicine; Health care; Economic growth; Computer science; Economics; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.4257245,0.002386777,0.006422145,0.01632224,0.002633229,0.03147072,0.01279612,0.008231492,0.00929282],"category_scores_gemma":[0.4926798,0.002284434,0.006135483,0.02949859,0.01394575,0.04659428,0.02172928,0.01807667,0.00411354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01572996,"about_ca_system_score_gemma":0.08050523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02444465,"about_ca_topic_score_gemma":0.02308,"domain_scores_codex":[0.695348,0.2010054,0.05412339,0.008590654,0.03734835,0.003584279],"domain_scores_gemma":[0.1510241,0.6322538,0.05937158,0.04842737,0.100499,0.008424226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006800027,0.0004031712,0.03752125,0.05069565,0.001498166,0.0004327339,0.008620542,0.003595414,0.0007605697,0.1239251,0.09950435,0.672363],"study_design_scores_gemma":[0.0002020555,0.0004642668,0.02974406,0.1668437,0.0009558373,0.0006430596,0.01336316,0.003327797,0.001614465,0.105893,0.676293,0.0006556868],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.009700158,0.2621647,0.0561948,0.61648,0.006954451,0.001502714,0.01589122,0.0006308318,0.03048117],"genre_scores_gemma":[0.1521866,0.3330049,0.3169685,0.1549188,0.00686313,0.005205948,0.02727087,0.0007564048,0.002824774],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.4257245,"threshold_uncertainty_score":0.708184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3189376319464468,"score_gpt":0.5490738713682429,"score_spread":0.2301362394217961,"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."}}