BackgroundArthroscopic partial meniscectomy is one of the most common orthopedic procedures, yet rigorous evidence of its efficacy is lacking.MethodsWe conducted a multicenter, randomized, double-blind, sham-controlled trial in 146 patients 35 to 65 years of age who had knee symptoms consistent with a degenerative medial meniscus tear and no knee osteoarthritis. Patients were randomly assigned to arthroscopic partial meniscectomy or sham surgery. The primary outcomes were changes in the Lysholm and Western Ontario Meniscal Evaluation Tool (WOMET) scores (each ranging from 0 to 100, with lower scores indicating more severe symptoms) and in knee pain after exercise (rated on a scale from 0 to 10, with 0 denoting no pain) at 12 months after the procedure.ResultsIn the intention-to-treat analysis, there were no significant between-group differences in the change from baseline to 12 months in any primary outcome. The mean changes (improvements) in the primary outcome measures were as follows: Lysholm score, 21.7 points in the partial-meniscectomy group as compared with 23.3 points in the sham-surgery group (between-group difference, −1.6 points; 95% confidence interval [CI], −7.2 to 4.0); WOMET score, 24.6 and 27.1 points, respectively (between-group difference, −2.5 points; 95% CI, −9.2 to 4.1); and score for knee pain after exercise, 3.1 and 3.3 points, respectively (between-group difference, −0.1; 95% CI, −0.9 to 0.7). There were no... |
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Showing posts with label Confidence interval. Show all posts
Showing posts with label Confidence interval. Show all posts
Friday, December 27, 2013
Arthroscopic Partial Meniscectomy versus Sham Surgery for a Degenerative Meniscal Tear
Sunday, September 15, 2013
Worrisome or not? Lung nodules identified on initial LDCT lung cancer screening
Long the domain of astrologers and tarot card readers, prediction has recently become downright fashionable. While quant-minded individuals like Billy Beane and Nate Silver have achieved fame and fortune using probabilistic forecasting, dozens of smartphone apps deliver the predictive insight of clinical risk scores to doctors’ fingertips. Why all the enthusiasm? Accurate predictions allow us to prepare for the future.
Testing their predictive mettle in this week’s NEJM, Dr. Annette McWilliams (British Columbia Cancer Agency, Vancouver, Canada) and colleagues ask a deceptively simple research question: If a low-dose computed tomography (LDCT) lung cancer screening test detects a lung nodule, can we use the information at hand to accurately predict if it is malignant? Using clinical and LDCT data from 1871 current or former smokers in the PanCan study, the investigators developed a model to predict when a newly discovered nodule was cancerous. Model variables included age, family history of lung cancer, and the presence of emphysema as well as nodule size, type, and location. Next, the investigators tested this prediction model in a cohort of 1090 current and former smokers enrolled in several British Columbia Cancer Agency chemoprevention trials. They found their model successfully discriminated between higher-risk and lower-risk nodules even within this validation cohort (AUC = 0.97, 95%CI 0.95-0.99), suggesting that the model can also be generalized to other... |
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