{"id":"W4391361259","doi":"10.1080/14787210.2024.2313054","title":"The clinical application of traditional Chinese medicine NRICM101 in hospitalized patients with COVID-19","year":2024,"lang":"en","type":"article","venue":"Expert Review of Anti-infective Therapy","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Mechanical ventilation; Intubation; Odds ratio; Propensity score matching; Coronavirus disease 2019 (COVID-19); Confounding; Retrospective cohort study; Diarrhea; Anesthesia; Ventilation (architecture); Internal medicine; Disease","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"],"consensus_categories":[],"category_scores_codex":[0.002698647,0.0002208229,0.001094481,0.0001249469,0.00006830445,0.000005261244,0.0001911439,0.00006807927,0.0001326427],"category_scores_gemma":[0.01653591,0.00009997052,0.0002913335,0.0009109778,0.001048229,0.00007525987,0.00004318394,0.0003285532,0.00000655933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001530595,"about_ca_system_score_gemma":0.0005070151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001945108,"about_ca_topic_score_gemma":0.000008434098,"domain_scores_codex":[0.9970971,0.0004174387,0.001072888,0.0004170762,0.0007864797,0.0002090582],"domain_scores_gemma":[0.9854207,0.01318319,0.0002703159,0.0004842494,0.0004618344,0.0001796977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001430264,0.002543149,0.8257692,0.007557215,0.0009011815,0.00001665889,0.0009407004,0.000002371964,0.0003266902,0.002115187,0.007125061,0.1512723],"study_design_scores_gemma":[0.004931626,0.003416192,0.8854647,0.00476002,0.00004524978,0.000001128398,0.00002010026,0.00008960627,0.00002570406,0.001293019,0.09981548,0.0001371686],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5099791,0.3887061,0.007818636,0.08247508,0.0006440366,0.009257232,0.00007576286,0.0001467556,0.0008973077],"genre_scores_gemma":[0.5845147,0.4113798,0.00007270261,0.003537453,0.0002074486,0.0002219211,0.00003788415,0.00001765743,0.0000104493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1511352,"threshold_uncertainty_score":0.9917482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05721798247895921,"score_gpt":0.4912762297733777,"score_spread":0.4340582472944185,"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."}}