{"id":"W1991343203","doi":"10.1007/s10654-011-9551-z","title":"Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration","year":2011,"lang":"en","type":"article","venue":"European Journal of Epidemiology","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"National Cancer Institute; U.S. Department of Health and Human Services","keywords":"Medicine; Checklist; Multidisciplinary approach; Transparency (behavior); Public health; Risk assessment; Epidemiology; Strengths and weaknesses; Quality (philosophy); Disease; Risk analysis (engineering); Data science; Management science; Pathology; Computer science; Psychology","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":["metaresearch"],"category_scores_codex":[0.7538769,0.005565057,0.007438097,0.02184352,0.004423921,0.01034911,0.01046771,0.01604467,0.005208639],"category_scores_gemma":[0.8888552,0.008213371,0.01411539,0.02008778,0.01119854,0.01846164,0.01841206,0.02046478,0.002767326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01151921,"about_ca_system_score_gemma":0.04596571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005784367,"about_ca_topic_score_gemma":0.004041061,"domain_scores_codex":[0.1272739,0.4356002,0.3884282,0.006482959,0.0392512,0.002963539],"domain_scores_gemma":[0.04998768,0.5903479,0.136272,0.07856776,0.1427847,0.002039948],"domain_codex":"methods","domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002300087,0.0003370282,0.01559535,0.191868,0.005633674,0.001394228,0.03693067,0.007009391,0.003599863,0.07912912,0.2592424,0.3969601],"study_design_scores_gemma":[0.003190158,0.001136731,0.01671233,0.2634338,0.00698246,0.001549272,0.007178409,0.02137793,0.009412294,0.1801391,0.487531,0.001356387],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007051842,0.0155545,0.6454592,0.2186746,0.01370388,0.0829106,0.008649208,0.002655652,0.005340583],"genre_scores_gemma":[0.02306722,0.009158311,0.8309719,0.02509355,0.003388357,0.1040222,0.002999028,0.0003211372,0.0009782638],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2461231,"threshold_uncertainty_score":0.3035137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8764955356768007,"score_gpt":0.5367980619107815,"score_spread":0.3396974737660192,"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."}}