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Background: The most frequent site of metastases from prostate cancer is bone. Adjuvant bisphosphonate treatment improves outcomes of patients with bone metastasis-negative breast cancer, but the effects of bisphosphonates on bone metastases in prostate cancer are not known.Methods: We performed a randomized double-blind placebo-controlled trial to determine whether a first-generation bisphosphonate could improve symptomatic bone metastasis-free survival (time to symptomatic bone metastases or death from prostate cancer) in men with nonmetastatic prostate cancer who were at high risk of developing bone metastases. Between June 1, 1994, and December 31, 1997, 508 men from 26 UK sites and one New Zealand site who were within 3 years of initial prostate cancer diagnosis with no evidence of metastases from current bone scanning were randomly assigned to daily oral sodium clodronate (2080 mg/day, n = 254) or placebo (n = 254) for a maximum of 5 years. Estimates of outcome risks were compared using Kaplan-Meier analyses.Results: The groups allocated to each treatment were well balanced. After a median follow-up of nearly 10 years, no evidence of benefit to the clodronate group was observed in terms of bone metastases-free survival (clodronate versus placebo, 80 events versus 68 events; hazard ratio [HR] = 1.22; 95% confidence interval [CI] = 0.88 to 1.68) or overall survival (clodronate versus placebo, 130 deaths versus 127 deaths; HR = 1.02; 95% CI = 0.80 to 1.30). Adverse events, notably gastrointestinal problems and increased lactate dehydrogenase levels, were more frequent in the clodronate group than in the placebo group; otherwise, clodronate was well tolerated. Modification of trial drug dose was more frequent in the clodronate group than the placebo group (HR = 1.63, 95% CI = 1.21 to 2.19).Conclusion: Adjuvant sodium clodronate does not modify the natural history of nonmetastatic prostate cancer. [ABSTRACT FROM AUTHOR]

As part of the 2006 i2b2 NLP Shared Task, we explored two methods for determining the smoking status of patients from their hospital discharge summaries when explicit smoking terms were present and when those same terms were removed. We developed a simple keyword-based classifier to determine smoking status from de-identified hospital discharge summaries. We then developed a Naïve Bayes classifier to determine smoking status from the same records after all smoking-related words had been manually removed (the smoke-blind dataset). The performance of the Naïve Bayes classifier was compared with the performance of three human annotators on a subset of the same training dataset (n = 54) and against the evaluation dataset (n = 104 records). The rule-based classifier was able to accurately extract smoking status from hospital discharge summaries when they contained explicit smoking words. On the smoke-blind dataset, where explicit smoking cues are not available, two Naïve Bayes systems performed less well than the rule-based classifier, but similarly to three expert human annotators. [ABSTRACT FROM AUTHOR]