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Biomedical subjects

J A Baker

Publications and source records attributed to J A Baker.

103 records · Page 6Linked to original sources
PubMedDate not supplied

Active and passive smoking and risk of ovarian cancer.

It is unclear whether smoking is a risk factor for epithelial ovarian cancer, although some studies have suggested that it may be associated with an increased risk of mucinous tumors. This study investigated the effect of smoking and environmental tobacco smoke (ETS) on ovarian cancer risk among 434 women with primary epithelial ovarian, peritoneal, or fallopian cancers and 868 age- and region-matched hospital controls with nonneoplastic conditions. All participants completed a comprehensive epidemiologic questionnaire. Results indicate that decreased risk of ovarian cancer was associated with being a nonsmoker exposed to ETS (adjusted odds ratio [aOR] 0.68, 95% confidence interval [CI] 0.46-0.99), a former smoker (aOR 0.76, 95% CI 0.53-1.10), or a current smoker (aOR 0.53, 95% CI 0.32-0.88). A similar protective effect was noted for smokers with moderate or high exposure based on smoking intensity, duration, and cumulative exposure, as well as for never smokers exposed to ETS. Results did not differ substantially by histologic subtype. Although prevailing theories of ovarian cancer etiology implicate incessant ovulation, characteristics of the study population suggest that anovulation was not the protective mechanism in this study. Immunosuppression by nicotine or upregulation of enzymes that metabolize carcinogens may be responsible for the effects observed.

Adult↗
PubMedDate not supplied

An artificial neural network for estimating scatter exposures in portable chest radiography.

An adaptive linear element (Adaline) was developed to estimate the two-dimensional scatter exposure distribution in digital portable chest radiographs (DPCXR). DPCXRs and quantitative scatter exposure measurements at 64 locations throughout the chest were acquired for ten radiographically normal patients. The Adaline is an artificial neural network which has only a single node and linear thresholding. The Adaline was trained using DPCXR-scatter measurement pairs from five patients. The spatially invariant network would take a portion of the image as its input and estimate the scatter content as output. The trained network was applied to the other five images, and errors were evaluated between estimated and measured scatter values. Performance was compared against a convolution scatter estimation algorithm. The network was evaluated as a function of network size, initial values, and duration of training. Network performance was evaluated qualitatively by the correlation of network weights to physical models, and quantitatively by training and evaluation errors. Using DPCXRs as input, the network learned to describe known scatter exposures accurately (7% error) and estimate scatter in new images (< 8% error) slightly better than convolution methods. Regardless of size and initial shape, all networks adapted into radial exponentials with magnitude of 0.75, perhaps implying an ideal point spread function and average scatter fraction, respectively. To implement scatter compensation, the two-dimensional scatter distribution estimated by the neural network is subtracted from the original DPCXR.

Algorithms↗
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Marketing guidelines for medical group practice.

For some groups, patients are no longer available in an endless supply. Administrators are learning that they must respond to changing market situations in order to maintain their group's fair share of the market.

Group Practice↗