{"id":"W4252813111","doi":"10.23970/ahrqepcmethguide1","title":"Prioritization and Selection of Harms for Inclusion in Systematic Reviews","year":2017,"lang":"en","type":"report","venue":"","topic":"Delphi Technique in Research","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Vanderbilt University; Agency for Healthcare Research and Quality; Johns Hopkins University; U.S. Department of Health and Human Services","keywords":"Prioritization; Selection (genetic algorithm); Inclusion (mineral); Systematic review; Computer science; Actuarial science; Risk analysis (engineering); Data science; Management science; Psychology; Business; Political science; Economics; Artificial intelligence; MEDLINE; Social psychology; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01725226,0.00009159012,0.0005924085,0.0002564679,0.0004232564,0.00006786173,0.0002932853,0.0003234927,0.00002268077],"category_scores_gemma":[0.01585593,0.00007448286,0.00006491126,0.0001642606,0.0001024629,0.0001348018,0.0003495135,0.0001370265,0.000001131215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000440664,"about_ca_system_score_gemma":0.00110089,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007589895,"about_ca_topic_score_gemma":0.009243335,"domain_scores_codex":[0.9977376,0.0003952395,0.0006208699,0.0002010568,0.0008858434,0.0001593396],"domain_scores_gemma":[0.998029,0.0003707928,0.0006887206,0.0001575012,0.0007162488,0.00003771561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003838556,0.0002226457,0.004856497,0.6665727,0.00006375802,0.000005626634,0.01059872,8.137588e-7,0.001593153,0.006757794,0.1659695,0.1433204],"study_design_scores_gemma":[0.000580999,0.0003063344,0.001029747,0.1307768,0.0001988058,0.00001530382,0.001271593,0.0006750628,0.001059346,0.02364294,0.8395984,0.0008446724],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.00133761,0.02198619,0.02230352,0.002260639,0.001518454,0.0645456,0.00003466138,0.0003155657,0.8856978],"genre_scores_gemma":[0.4333536,0.3495118,0.0179301,0.00005419572,0.001509031,0.006635338,0.0000891552,0.0001382882,0.1907785],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6949192,"threshold_uncertainty_score":0.9990187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3323301262150563,"score_gpt":0.5570469542739996,"score_spread":0.2247168280589433,"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."}}