{"id":"W4403964361","doi":"10.1186/s12245-024-00738-7","title":"Bibliometric analysis of the usage of tenecteplase for stroke","year":2024,"lang":"en","type":"article","venue":"International Journal of Emergency Medicine","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Tenecteplase; Angiology; Stroke (engine); MEDLINE; Ischaemic stroke; Emergency medicine; Internal medicine; Myocardial infarction; Thrombolysis; Atrial fibrillation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"not_applicable","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007153005,0.0005403293,0.0018467,0.148741,0.0009482666,0.003626849,0.001004595,0.0006679543,0.005355977],"category_scores_gemma":[0.06254223,0.000241876,0.002299952,0.1923258,0.0006676523,0.00251906,0.001437163,0.0003888388,0.001057679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002768378,"about_ca_system_score_gemma":0.004087674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007086963,"about_ca_topic_score_gemma":0.007801728,"domain_scores_codex":[0.9850593,0.002725707,0.003838223,0.001221167,0.006647533,0.0005081321],"domain_scores_gemma":[0.9106182,0.05055802,0.0210852,0.001881218,0.01485431,0.001002996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004593045,0.0001769164,0.7320903,0.03393345,0.003687896,0.0007993215,0.002940504,0.001925921,0.00142213,0.00465951,0.0231289,0.1947759],"study_design_scores_gemma":[0.00006139647,0.0001824482,0.9277128,0.004688231,0.00300167,0.001569421,0.005015727,0.003415057,0.001171933,0.002354137,0.05072325,0.000104],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7041847,0.1133811,0.002525576,0.003635734,0.0002995325,0.0008147362,0.1265547,0.0004844835,0.04811939],"genre_scores_gemma":[0.9242012,0.03880442,0.003091865,0.0001890424,0.0003192904,0.0006250049,0.03060296,0.00005799165,0.002108262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8512591,"threshold_uncertainty_score":0.03782916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04703422917965209,"score_gpt":0.385305652689085,"score_spread":0.3382714235094328,"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."}}