{"id":"W2624445525","doi":"10.1016/j.jclinepi.2017.06.003","title":"Graphic report of the results from propensity score method analyses","year":2017,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Jewish General Hospital","funders":"Fonds de Recherche du Québec-Société et Culture; Jewish General Hospital","keywords":"Propensity score matching; Statistics; Medicine; Computer science; Medical physics; Mathematics","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":[0.006979305,0.002400442,0.002189297,0.007571201,0.0004544435,0.002182382,0.001128522,0.00171248,0.2183285],"category_scores_gemma":[0.0759767,0.0006470899,0.002791296,0.004935165,0.000867644,0.0008828019,0.001246823,0.002435587,0.0127494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006769303,"about_ca_system_score_gemma":0.001785126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01221982,"about_ca_topic_score_gemma":0.007679987,"domain_scores_codex":[0.9940123,0.003094541,0.0004905477,0.0006263977,0.00144656,0.0003296332],"domain_scores_gemma":[0.8771523,0.1024379,0.004799467,0.005055579,0.01002413,0.000530668],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003667195,0.0007067203,0.01530195,0.004147829,0.001741795,0.0006824098,0.0003358184,0.01497812,0.00153123,0.0220059,0.7846573,0.1502438],"study_design_scores_gemma":[0.008677034,0.002274057,0.2109237,0.005674107,0.003820246,0.0042446,0.001385562,0.1765726,0.007947313,0.1122501,0.4652435,0.0009873029],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.05760099,0.004282974,0.2811041,0.01319477,0.008916904,0.004164953,0.5301064,0.0325806,0.06804843],"genre_scores_gemma":[0.3924133,0.003643274,0.3700648,0.008690302,0.00291633,0.006097144,0.1553852,0.0079289,0.05286086],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9930207,"threshold_uncertainty_score":0.7303815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8981984549665861,"score_gpt":0.6914254145489367,"score_spread":0.2067730404176494,"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."}}