{"id":"W3160848562","doi":"10.1177/0272989x211073098","title":"Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation","year":2022,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Imperfect; Monte Carlo method; Matching (statistics); Sampling (signal processing); Sample (material); Moment (physics); Value of information; Expected value","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1091583,0.0001866567,0.001559043,0.0005963155,0.000547328,0.0002714419,0.00130868,0.000062279,0.03353796],"category_scores_gemma":[0.1842829,0.0001111484,0.001023823,0.001683409,0.00002841341,0.0003993677,0.000507571,0.0001951755,0.00006476399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009539274,"about_ca_system_score_gemma":0.000153036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003845538,"about_ca_topic_score_gemma":0.00001747794,"domain_scores_codex":[0.9732031,0.003770407,0.01014601,0.0005292824,0.01203925,0.0003119308],"domain_scores_gemma":[0.9599207,0.031523,0.005927958,0.001438645,0.001030379,0.0001593024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002787702,0.00002099649,0.01173024,0.00003876931,0.00004817512,0.000001195325,0.001301005,0.00326604,0.00006059763,0.004601929,0.01363685,0.9652663],"study_design_scores_gemma":[0.001235809,0.000246223,0.01282355,0.0001436473,0.0001704228,0.00003055141,0.01437941,0.857803,0.00007553642,0.02831651,0.08447745,0.0002978346],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3090828,0.00007735764,0.6892604,0.0001500468,0.0003912716,0.0006540408,0.00006112816,0.000007975294,0.0003150347],"genre_scores_gemma":[0.9253876,0.000001460997,0.07376205,0.0005301225,0.00007703094,0.0001493163,0.00005988151,0.000009193767,0.00002331074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9649685,"threshold_uncertainty_score":0.9673455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5071110705585236,"score_gpt":0.5631016622128868,"score_spread":0.05599059165436326,"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."}}