AIM:To compare a first diagnostic procedure of transbronchial needle aspiration(TBNA)with selection of endoscopic ultrasound-guided fine-needle aspiration(EUS-FNA)or TBNA for mediastinal lymphadenopathy.METHODS:Sixty-...AIM:To compare a first diagnostic procedure of transbronchial needle aspiration(TBNA)with selection of endoscopic ultrasound-guided fine-needle aspiration(EUS-FNA)or TBNA for mediastinal lymphadenopathy.METHODS:Sixty-eight consecutive patients with mediastinal lymphadenopathy on computed tomography(CT),who required cytopathological diagnosis,were recruited.The first 34 underwent a sequential approach in which TBNA was performed first,followed by EUS-FNA if TBNA was unrevealing.The next 34 underwent a selective approach where either TBNA or EUS-FNA was selected as the first procedure based on the CT findings.RESULTS:The diagnostic yield of TBNA as the first diagnostic procedure in the sequential approach was 62%.In the selective approach,the diagnostic yield of the first procedure was 71%.There was no significant difference in the overall diagnostic yield,but there were significantly fewer combined procedures with the selective approach.CONCLUSION:Selecting either EUS-FNA or TBNA as the first diagnostic procedure achieved a comparable diagnostic yield with significantly fewer procedures than performing TBNA first in all patients.展开更多
This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) ...This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.展开更多
文摘AIM:To compare a first diagnostic procedure of transbronchial needle aspiration(TBNA)with selection of endoscopic ultrasound-guided fine-needle aspiration(EUS-FNA)or TBNA for mediastinal lymphadenopathy.METHODS:Sixty-eight consecutive patients with mediastinal lymphadenopathy on computed tomography(CT),who required cytopathological diagnosis,were recruited.The first 34 underwent a sequential approach in which TBNA was performed first,followed by EUS-FNA if TBNA was unrevealing.The next 34 underwent a selective approach where either TBNA or EUS-FNA was selected as the first procedure based on the CT findings.RESULTS:The diagnostic yield of TBNA as the first diagnostic procedure in the sequential approach was 62%.In the selective approach,the diagnostic yield of the first procedure was 71%.There was no significant difference in the overall diagnostic yield,but there were significantly fewer combined procedures with the selective approach.CONCLUSION:Selecting either EUS-FNA or TBNA as the first diagnostic procedure achieved a comparable diagnostic yield with significantly fewer procedures than performing TBNA first in all patients.
基金Project (No. 28-05-03-03) supported by the Yildiz Technical University Research Fund, Turkey
文摘This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.
