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31.
Denise Lee Marcella D. Walker Hsin Yi Chen John A. Chabot James A. Lee Jennifer H. Kuo 《Surgery》2019,165(1):107-113
Background
Bone mineral density (BMD) has been found to improve after parathyroidectomy (PTX) in patients with primary hyperparathyroidism. There are few data on the effect of PTX on BMD in normocalcemic and normohormonal primary hyperparathyroidism.Methods
A retrospective analysis of 92 primary hyperparathyroidism patients who underwent PTX between 2004 and 2012 with pre- and post-PTX dual-energy x-ray absorptiometry was performed. Within-person changes in BMD pre- and post-PTX were analyzed using log linear mixed models, stratified by biochemical status.Results
Bone mineral density increased post-PTX in the whole cohort at the lumbar spine (+2.5%), femoral neck (+2.1%), and total hip (+1.9%) and decreased at the one-third radius (–0.9%). On comparison of BMD changes by profile, BMD increased in those with the typical profile at the lumbar spine (3.2%), femoral neck (2.9%), and total hip (2.9%) but declined at the one-third radius (–1.5%). In contrast, BMD improved only at the femoral neck (4.3%) in the normohormonal group and did not change at any site in the normocalcemic group. The typical group had a greater increase in BMD over time at the femoral neck and total hip compared with normocalcemic patients.Conclusion
Our results indicate that the skeletal benefit of PTX was attenuated in normocalcemic and normohormonal patients, suggesting that skeletal changes after PTX may depend on biochemical profile. 相似文献32.
33.
A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity. 相似文献
34.
Evaluation of training nurses to perform semi‐automated three‐dimensional left ventricular ejection fraction using a customised workstation‐based training protocol 下载免费PDF全文
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Xinran Liu James Anstey Ron Li Chethan Sarabu Reiri Sono Atul J. Butte 《Applied clinical informatics》2021,12(2):407
Background Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers. 相似文献
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Molly Orcutt Wendy C. King Melissa A. Kalarchian Michael J. Devlin Marsha D. Marcus Luis Garcia Kristine J. Steffen James E. Mitchell 《Surgery for obesity and related diseases》2019,15(2):295-303