{"id":"W6958458997","doi":"10.6084/m9.figshare.c.3789454_d2.v1","title":"Additional file 2: of Engaging the Canadian public on reimbursement decision-making for drugs for rare diseases: a national online survey","year":2017,"lang":"en","type":"article","venue":"Figshare","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reimbursement; Public access; Data collection; Government (linguistics); MEDLINE","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003419774,0.0007077415,0.0009310241,0.005136462,0.002371802,0.001991426,0.001730145,0.0008097007,0.6391814],"category_scores_gemma":[0.05573978,0.0005254057,0.0008947414,0.01151505,0.0005404969,0.00209631,0.001442649,0.001145304,0.0438755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01608028,"about_ca_system_score_gemma":0.0292678,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8172706,"about_ca_topic_score_gemma":0.8799703,"domain_scores_codex":[0.9975066,0.0004072674,0.0003644814,0.0002291362,0.0009365885,0.0005560705],"domain_scores_gemma":[0.9423426,0.03101527,0.003488031,0.001812989,0.01889096,0.002450079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00008463202,0.00004355262,0.007020706,0.0006855662,0.00001732333,0.00002548837,0.0001748461,0.0001320969,0.00001109575,0.0004063302,0.9839665,0.007431676],"study_design_scores_gemma":[0.001792448,0.0001361752,0.2619747,0.004707578,0.0001994947,0.0002850582,0.005267279,0.001991694,0.0003796912,0.002937079,0.720095,0.0002338416],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.001170669,0.00003695577,0.0001036837,0.0005012032,0.00002382897,0.0002719539,0.9917402,0.000105037,0.006046403],"genre_scores_gemma":[0.08475143,0.0009213937,0.006825099,0.003250902,0.0001756942,0.007838848,0.8545064,0.0005947201,0.04113549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6391814,"threshold_uncertainty_score":0.5146639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.554390301318173,"score_gpt":0.4651230569487683,"score_spread":0.0892672443694047,"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."}}