{"id":"W4307343125","doi":"10.1002/jrsm.1608","title":"Network meta‐interpolation: Effect modification adjustment in network meta‐analysis using subgroup analyses","year":2022,"lang":"en","type":"article","venue":"Research Synthesis Methods","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Statistics; Meta-analysis; Standard error; Mean squared error; Mathematics; Sample size determination; Subgroup analysis; Confidence interval; Data mining; Econometrics; Computer science; Medicine","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","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.04653263,0.0004269449,0.002481758,0.001310091,0.0006882597,0.00009776812,0.0008340998,0.0001436332,0.00277467],"category_scores_gemma":[0.007840147,0.0003536096,0.001431493,0.006988328,0.0001610207,0.00029301,0.0007297989,0.001057321,0.000004191271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007831217,"about_ca_system_score_gemma":0.0001071223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004300803,"about_ca_topic_score_gemma":0.00009271006,"domain_scores_codex":[0.9605968,0.03471208,0.00105394,0.0009145213,0.001549611,0.001173048],"domain_scores_gemma":[0.9407361,0.05703649,0.0004193638,0.001387212,0.0002524639,0.000168358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005175425,0.0007172277,0.0008421571,0.000372727,0.3171486,0.00005501145,0.0006749143,0.5770499,0.01688294,0.06239987,0.0017972,0.02154194],"study_design_scores_gemma":[0.0001394507,0.0002467117,0.0002741283,0.00002563199,0.1883855,0.000007774099,0.0003074675,0.3287554,0.01430863,0.4665262,0.0004107156,0.0006124696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002267495,0.01078944,0.9841436,0.00021841,0.00008014324,0.001434303,0.00002050398,0.0002647518,0.0007814248],"genre_scores_gemma":[0.1863977,0.0001206778,0.8096294,0.00003673859,0.0001612523,0.003441889,0.00001076006,0.00006967527,0.0001318613],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4041263,"threshold_uncertainty_score":0.9998916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8152217341987545,"score_gpt":0.6479104043905357,"score_spread":0.1673113298082188,"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."}}