{"id":"W3049362923","doi":"","title":"급성 두통환자의 거미막밑출혈 예측을 위한 호중구/림프구 비율 및 임상 예측 지표방법의 유용성","year":2018,"lang":"ko","type":"article","venue":"대한응급의학회지","topic":"Neurosurgical Procedures and Complications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Clinical prediction rule; Receiver operating characteristic; Subarachnoid haemorrhage; Subarachnoid hemorrhage; Blood pressure; Emergency department; Internal medicine; Anesthesia; Surgery","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":[0.002490675,0.0004312324,0.0003930216,0.0008931507,0.0002008496,0.0009313963,0.0003256742,0.000357796,0.001300679],"category_scores_gemma":[0.008135229,0.0001501138,0.0003789987,0.0004757493,0.0003715545,0.000793725,0.0003073431,0.0003390213,0.0004772864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003387058,"about_ca_system_score_gemma":0.0005622277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00116499,"about_ca_topic_score_gemma":0.001475026,"domain_scores_codex":[0.9986398,0.0003835734,0.0002783393,0.0002441156,0.0003018715,0.000152304],"domain_scores_gemma":[0.9956101,0.001521381,0.001619403,0.0001150973,0.0008579506,0.0002761127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004498936,0.00005476168,0.9945877,0.00003246971,0.00006184687,0.00005457462,0.00002430081,0.00008732251,0.0001591983,0.00001385041,0.00007287766,0.004401136],"study_design_scores_gemma":[0.00006570689,0.0013582,0.9932523,0.00005279561,0.0002251441,0.001393515,0.0003356214,0.001787337,0.0007827993,0.00006775084,0.0006617975,0.00001695114],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967128,0.001510192,0.0003959112,0.00009987984,0.00002604686,0.00004281369,0.000268646,0.000009179621,0.0009345661],"genre_scores_gemma":[0.9988993,0.0001760649,0.0004578419,0.00003901842,0.00003742586,0.00001765315,0.000217138,0.000001324825,0.0001541637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002490675,"threshold_uncertainty_score":0.01317209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02738397035837124,"score_gpt":0.3107075809196771,"score_spread":0.2833236105613058,"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."}}