{"id":"W4406497222","doi":"10.1177/20552076251314102","title":"The impacts on population health by China's regional health data centers and the potential mechanism of influence","year":2025,"lang":"en","type":"article","venue":"Digital Health","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Mechanism (biology); China; Population health; Population; Environmental health; Business; Geography; 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":[],"consensus_categories":[],"category_scores_codex":[0.01455427,0.0003614982,0.0004688878,0.001453778,0.001195363,0.002654746,0.001368415,0.0006149866,0.003356207],"category_scores_gemma":[0.0276089,0.0003096053,0.001101321,0.002362836,0.002354235,0.001663857,0.003541943,0.001138896,0.0002103306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004239479,"about_ca_system_score_gemma":0.00641561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04310003,"about_ca_topic_score_gemma":0.03304321,"domain_scores_codex":[0.9899055,0.006224872,0.0003963738,0.001040346,0.001194027,0.001238885],"domain_scores_gemma":[0.9697627,0.01471643,0.006007157,0.003683391,0.003735286,0.002095058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002790955,0.0002053887,0.8783825,0.0004602618,0.0004793178,0.0005122056,0.002198009,0.008055856,0.001047133,0.03888825,0.006436448,0.06305565],"study_design_scores_gemma":[0.0001606702,0.0003177144,0.9245781,0.0003844816,0.0004921308,0.0001658282,0.002927409,0.0243801,0.002594202,0.0112963,0.03259498,0.0001081562],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9377443,0.003163322,0.01419348,0.01609874,0.0003524086,0.0005052359,0.003932247,0.0001920846,0.02381831],"genre_scores_gemma":[0.9960725,0.0003790518,0.00181208,0.0005860213,0.00007835892,0.0001089654,0.0003382329,0.000009745134,0.0006150501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04310003,"threshold_uncertainty_score":0.08569831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257283052040192,"score_gpt":0.2813547973665225,"score_spread":0.2587819668461206,"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."}}