{"id":"W4313522766","doi":"10.21203/rs.3.rs-2192562/v1","title":"COVID-19 individual participant data meta-analyses. Can there be too many? Results from a rapid systematic review.","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Long-Term Effects of COVID-19","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Expediting; Data extraction; Coronavirus disease 2019 (COVID-19); Leverage (statistics); Meta-analysis; Data collection; Aggregate data; Systematic review; Inference; Computer science; Medicine; Psychology; MEDLINE; Artificial intelligence; Statistics; Engineering; Political science; Internal 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.1705774,0.003816345,0.01725393,0.02552165,0.001855944,0.01047203,0.007638765,0.005581524,0.04625874],"category_scores_gemma":[0.4937977,0.004647915,0.0249375,0.02396536,0.00216475,0.008952455,0.00924754,0.004732156,0.005495442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008083752,"about_ca_system_score_gemma":0.02843639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004945315,"about_ca_topic_score_gemma":0.01089151,"domain_scores_codex":[0.8215355,0.08214735,0.06935029,0.008432361,0.01684829,0.001686171],"domain_scores_gemma":[0.5426744,0.3334486,0.05341474,0.02448826,0.04344642,0.002527551],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0006420004,0.00001256333,0.0003689285,0.9494511,0.01322801,0.00006478497,0.0003409952,0.0002161846,0.0001899738,0.001219137,0.01402291,0.02024328],"study_design_scores_gemma":[0.004536985,0.0003973789,0.003152104,0.7881837,0.07651836,0.0002486589,0.0004125483,0.0005320719,0.0005482663,0.007911553,0.1173115,0.0002469569],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.004051033,0.6145753,0.0370558,0.01804364,0.004927699,0.1687657,0.140471,0.003037405,0.009072556],"genre_scores_gemma":[0.03290693,0.2042861,0.1589181,0.01066303,0.0009430184,0.5625377,0.02534767,0.001423596,0.002973726],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9827461,"threshold_uncertainty_score":0.9021103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6774342297446776,"score_gpt":0.5639008329969333,"score_spread":0.1135333967477443,"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."}}