{"id":"W4390496690","doi":"10.17504/protocols.io.rm7vzxeyrgx1/v1","title":"Novel Clinical Prediction Model: Integrating A2DS2score with 24-hour ASPECTS and Red Cell Distribution Width for EnhancedPrediction of Stroke-Associated Pneumonia following Intravenous Thrombolysis v1","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Thrombolysis; Logistic regression; Medicine; Red blood cell distribution width; Stroke (engine); Receiver operating characteristic; Pneumonia; Retrospective cohort study; Internal medicine; Myocardial infarction; Cardiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003984533,0.001487389,0.001508166,0.002188138,0.0004322164,0.001910508,0.001586509,0.001177428,0.0030442],"category_scores_gemma":[0.006697075,0.000483149,0.00144234,0.001056754,0.000267352,0.0007666207,0.0009464353,0.001630861,0.001041398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009745525,"about_ca_system_score_gemma":0.002254034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01277134,"about_ca_topic_score_gemma":0.006987779,"domain_scores_codex":[0.998991,0.0004365689,0.00008044298,0.0002471192,0.000122601,0.0001224158],"domain_scores_gemma":[0.9962824,0.002338593,0.0003951025,0.000104959,0.0006146226,0.0002642949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002399638,0.001391477,0.6639235,0.0001543282,0.001205612,0.0006404468,0.0001669092,0.2062226,0.0008292109,0.001151216,0.009506273,0.1124087],"study_design_scores_gemma":[0.00007657718,0.0002320412,0.01627756,0.00002769393,0.0001736492,0.0001266041,0.00003380853,0.9818548,0.0001128227,0.0006709768,0.0003902742,0.00002320528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.848437,0.002010005,0.1352113,0.004072384,0.0004667195,0.00046095,0.003992313,0.001645398,0.003703861],"genre_scores_gemma":[0.9770239,0.0003909616,0.01804089,0.0001983603,0.0002516968,0.0002847748,0.002131661,0.00004464647,0.001633196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01277134,"threshold_uncertainty_score":0.02539402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224742795346263,"score_gpt":0.2888078918986791,"score_spread":0.2665604639452164,"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."}}