{"id":"W2367630464","doi":"","title":"Study of medical effectiveness of provincial stroke care delivery:based on Logistic regression","year":2014,"lang":"en","type":"article","venue":"Journal of Guangzhou University","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Logistic regression; Inflection point; Regression analysis; Statistics; Stroke (engine); Regression; Segmented regression; Medicine; Econometrics; Mathematics; Engineering; Nonlinear regression","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002622677,0.0001387188,0.0005826774,0.0003498729,0.0003421644,0.000001798718,0.0005428325,0.0003007117,0.0001080757],"category_scores_gemma":[0.001781928,0.0001113809,0.0001437085,0.0002388557,0.0001445841,0.00009671285,0.0001391459,0.0009344289,0.000007425852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004915401,"about_ca_system_score_gemma":0.001251157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002763472,"about_ca_topic_score_gemma":0.002234276,"domain_scores_codex":[0.9942993,0.003386105,0.0006927446,0.0001723891,0.001202248,0.0002472185],"domain_scores_gemma":[0.9940332,0.002823444,0.001164956,0.0002882801,0.001467515,0.0002226657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007705883,0.001385113,0.965677,0.001997041,0.00007899859,0.0002834001,0.01105704,0.001493003,0.0005279771,0.001174872,0.0002320086,0.008387616],"study_design_scores_gemma":[0.01131035,0.02759774,0.573964,0.01458559,0.0006156423,0.00001205359,0.3544399,0.007802001,0.007044488,0.0003002321,0.001695666,0.0006323304],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965997,0.00003322506,0.001112148,0.0001391086,0.000704481,0.0005817704,0.00002752718,0.00001248816,0.0007895262],"genre_scores_gemma":[0.9996373,0.00001303967,0.00009410708,0.00003725739,0.0001772318,5.443696e-7,0.000002047026,0.00001172819,0.00002670959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3917131,"threshold_uncertainty_score":0.454198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.112343878237889,"score_gpt":0.4441245728425358,"score_spread":0.3317806946046468,"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."}}