Biomedical subjects

Sara Golla

Publications and source records attributed to Sara Golla.

3 recordsLinked to original sources

PubMed2004

Do medical factors predict disability in older adults with persistent low back pain?

Persistent low back pain (LBP) is one of the most common and challenging persistent pain conditions in older adults. Medical comorbidity also is common in these individuals, but its impact on disability has not been examined. The purpose of this study was, using a cross-sectional design, to examine the functional impact of pain-related and general medical comorbidity on 100 community dwelling older adults (mean age 74.3) with persistent mechanical LBP. Subjects received a structured history and physical examination, lumbosacral spine X-rays, and standardized tests of physical function. Pain-related variables included intensity, duration, extent, and lumbar motion-induced pain. General medical variables included age, comorbidity, number of medications, depressive symptoms, back range of motion, body mass index, and severity of radiographic pathology. Function/disability measures included self-reported disability, gait speed, and mean number of static lifts and amount of work performed during a dynamic lifting task. Structural equation modeling was used to evaluate the influence of pain and medical variables on function/disability. The overall regression model indicated pain and medical variables were significantly associated with function/disability measures (R2=0.45, P<0.01). Individual regression coefficients, however, indicated that only pain duration (r=-0.36, P<0.05) and pain severity (r=0.37, P<0.001) were significantly associated with function/disability. Despite the prevalence of medical co-morbidities in older PLBP subjects, they appear to be of limited utility in understanding level of disability. These findings also underscore the need to optimize pain treatment in independent older adults to optimize physical function and delay the onset of dependent living status.

Aged↗

PubMed2004

Detection and classification performance levels of mammographic masses under different computer-aided detection cueing environments.

RATIONALE AND OBJECTIVES: The authors evaluated the impact of different computer-aided detection (CAD) cueing conditions on radiologists' performance levels in detecting and classifying masses depicted on mammograms. MATERIALS AND METHODS: In an observer performance study, eight radiologists interpreted 110 subtle cases six times under different display conditions to detect depicted masses and classify them as benign or malignant. Forty-five cases depicted biopsy-proven masses and 65 were negative. One mass-based cueing sensitivity of 80% and two false-positive cueing rates of 1.2 and 0.5 per image were used in this study. In one mode, radiologists first interpreted images without CAD results, followed by the display of cues and reinterpretation. In another mode, radiologists viewed CAD cues as images were presented and then interpreted images. Free-response receiver operating characteristic method was used to analyze and compare detection performance. The receiver operating characteristic method was used to evaluate classification performance. RESULTS: At these performance levels, providing cues after initial interpretation had little effect on the overall performance in detecting masses. However, in the mode with the highest false-positive cueing rate, viewing CAD cues immediately upon display of images significantly reduced average performance for both detection and classification tasks (P < .05). Viewing CAD cues during the initial display consistently resulted in fewer abnormalities being identified in noncued regions. CONCLUSION: CAD systems with low sensitivity (< or = 80% on mass-based detection) and high false-positive rate (> or = 0.5 per image) in a dataset with subtle abnormalities had little effect on radiologists' performance in the detection and classification of mammographic masses.

Area Under Curve↗

PubMed2003

Mammography with computer-aided detection: reproducibility assessment initial experience.

PURPOSE: To examine the performance and reproducibility of a commercially available computer-aided detection (CAD) system with a set of mammograms obtained in 100 patients who had undergone biopsy after positive findings at mammography. MATERIALS AND METHODS: One hundred positive mammographic examinations (four views each), depicting 96 masses and 50 microcalcification clusters, were scanned and analyzed three times by the CAD system. Reproducibility of detection sensitivity and the individual CAD-generated cues in the three images were examined. Both abnormality- and region-based detection sensitivities were compared. RESULTS: Forty-eight (96.0%) of 50 microcalcification clusters were marked on all three images in the abnormality-based analysis. Of the remaining two clusters, one was marked in two images and one was marked in only one. The abnormality-based sensitivity for mass detection ranged from 66.7% (64 of 96) to 70.8% (68 of 96). The system generated identical patterns (including images with and those without cues) for all three images in 53.3% (213 of 400) of images. For true-positive cluster regions, 88.9% (80 of 90) were marked at the same location in all images. For true-positive mass regions, 69.5% (82 of 118) were marked at the same locations in all images. In false-positive detections, only 44.0% (81 of 184) of false-positive mass regions and 31.9% (38 of 119) of false-positive cluster regions were marked at the same locations on all three images. CONCLUSION: Reproducibility of marked regions generated by the CAD system is improved from that reported previously, largely as a result of the substantial reduction in the false-positive detection rates. Reproducibility of true-positive identification of masses remains an important issue that may have methodologic and clinical practice implications.

Biopsy↗