Sentence examples for classify findings from inspiring English sources

Extra-spinal findings were reported by consensus in a structured database built on Microsoft Excel, using acronyms to classify findings according to specific organs and/or systems (e.g. vascular system, lymphatic system, kidney, uterus, ovaries, etc).

In a recently published analysis on chest radiographs, for example, deep convolutional neural networks being trained and validated with studies of 857 patients were found to reliably detect and classify findings of pulmonary tuberculosis in a test population (areas under the ROC-curves of 0.97 0.98) (Lakhani & Sundaram, 2017).

Single-view mammography was used for the screening and the Breast Imaging-Reporting and Data System (BI-RADS) was used to classify findings in the mammograms [ 17].

Overall, the present study suffers from lack of comparable piglets from non-NNPDS-affected herds in order to correctly classify findings as typical or diagnostic of NNPDS.

These schemes included the broad categories commonly used for classifying findings within a review of literature related to dairy cow mortality, a diagnostic scheme used within the problem-oriented veterinary medical record, and an analysis focusing on the primary physiologic system derangement for each death.

Classifying findings from each data source according to their quality can be helpful in understanding contradictory results.

We classified findings according to the components of the FITT framework (ie, user, technology, task) and the interfaces between components (table 1).

We classified findings into five categories: cancer, advanced adenoma (adenoma with significant (> 25%) villous features, or high grade dysplasia, including carcinoma-in-situ, or size 10 mm or larger [ 17, 18], adenomas 6 to 9 mm in size, adenomas ≤ 5 mm, and no adenoma found.

We classified findings into three categories: low (low risk of bias for all key quality domains), high (high risk of bias for one or more key domains) and unclear risk of bias (unclear risk of bias for one or more key domains).

He also cited a classified finding by the judge, based on a declaration and chart provided by the C.I.A. showing how productive Mr. Ghailani's interrogation had been.

Moreover, we classify these findings and introduce simple formatting and standards to supply predictive tools for oil spill models.