{"id":"W4386705315","doi":"10.1002/jrsm.1670","title":"How to plan and manage an individual participant data meta‐analysis. An illustrative toolkit","year":2023,"lang":"en","type":"article","venue":"Research Synthesis Methods","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University; Université de Montréal; McGill University Health Centre","funders":"Institute of Genetics; Horizon 2020 Framework Programme","keywords":"Computer science; Harmonization; Plan (archaeology); Multinational corporation; Aggregate data; Data collection; Knowledge management; Work (physics); Data extraction; Meta-analysis; Process management; MEDLINE; Medicine; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1683087,0.003692606,0.004407438,0.01100241,0.001580491,0.008468647,0.007083113,0.004796963,0.08274105],"category_scores_gemma":[0.403668,0.005520006,0.009570815,0.006344643,0.001824711,0.01077688,0.009020565,0.006578459,0.03652072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003333046,"about_ca_system_score_gemma":0.02327333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006429495,"about_ca_topic_score_gemma":0.008695784,"domain_scores_codex":[0.9106025,0.06372496,0.01406237,0.003279959,0.007568253,0.0007619778],"domain_scores_gemma":[0.5857114,0.3155445,0.01705748,0.04235404,0.03419266,0.005139851],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002594642,0.0002964883,0.002747251,0.0371462,0.003797711,0.001191958,0.005763727,0.01677746,0.002818385,0.03550059,0.4452799,0.4460857],"study_design_scores_gemma":[0.006039164,0.0004156579,0.003439355,0.02123598,0.002986446,0.001442989,0.001906422,0.0473826,0.005329909,0.2287222,0.6800238,0.001075447],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002024437,0.00372734,0.8403689,0.02070429,0.002096659,0.02144198,0.03517793,0.06169831,0.01276014],"genre_scores_gemma":[0.004331232,0.0009019101,0.9630758,0.0008815998,0.0001685647,0.02282954,0.002655303,0.003089779,0.002066227],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8316913,"threshold_uncertainty_score":0.8901123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.992173161325795,"score_gpt":0.7511059871365069,"score_spread":0.2410671741892881,"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."}}