Objective : We examined how the outcome of gastric bypass surgery (GBP) was effected by the interaction between presurgery eating disturbance status and length of time since surgery. Method: Subjects were recruited from a list of patients who received GBP in the last 3 years. Twenty-seven patients 20.8 ± 11.0 months postsurgery were interviewed. Results: Both current eating disturbance status and weight regain were predicted by the interaction between presurgical eating disturbance status and length of time since surgery. The significant time period in this interaction was 2 years or more postsurgery. Discussion: Patients with a presurgical eating disorder may experience a short-term improvement in their eating disorder following GBP that erodes on or after 2 years and is related to weight regain. , 1991), lead to substantial weight loss (Kral, 1992), questions remain about the long-term efficacy of these surgical techniques. Significant weight regain occurs in varying numbers of cases and appears to occur despite intact gastric restrictive procedures (RamseyStewart, 1995). Given the operative risk of bariatric surgery and the increased risk associated with reoperation of failed cases (Benotti & Forse, 1995), understanding and cor-

Objective We examined how the outcome of gastric bypass surgery (GBP) was effected by the interaction between presurgery eating disturbance status and length of time since surgery. Method: Subjects were recruited from a list of patients who received GBP in the last 3 years. Twenty‐seven patients 20.8 ± 11.0 months postsurgery were interviewed. Results: Both current eating disturbance status and weight regain were predicted by the interaction between presurgical eating disturbance status and length of time since surgery. The significant time period in this interaction was 2 years or more postsurgery. Discussion: Patients with a presurgical eating disorder may experience a short‐term improvement in their eating disorder following GBP that erodes on or after 2 years and is related to weight regain. Methods for improving surgical outcome in this population are discussed. © 1997 by John Wiley & Sons, Inc. Int J Eat Disord 21: 385–390, 1997.

Lead exposure from ingestion of bullet fragments is a serious environmental hazard to eagles. We determined blood lead levels (BLL) in 178 golden eagles (Aquila chrysaetos) captured during fall migration along a major North American flyway. These eagles spent the breeding season distributed over a large range and are the best currently available representation of free flying golden eagles on the continent. We found 58 % of these eagles containing increased BLL > 0.1 mg/L; 10 % were clinically lead poisoned with BLL > 0.6 mg/L; and 4 % were lethally exposed with BLL > 1.2 mg/L. No statistical difference in BLL existed between golden and bald eagles (Haliaeetus leucocephalus). Golden eagles captured on carrion had higher BLL than those captured using live bait suggesting differences in feeding habits among individuals. Median BLL increased with age class. We propose a conceptual model for the long-term increase in BLL after ingestion of lead particles. The mean blood mercury level in golden eagles was 0.023 mg/L. We evaluate a field test for BLL that is based on anodic stripping voltammetry. This cost-effective and immediate method correlated well with results from inductively coupled plasma-mass spectrometry, although results needed to be corrected for each calibration of the test kit.

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[1] This work focuses on different methods to generate confidence regions for nonlinear parameter identification problems. Three methods for confidence region estimation are considered: a linear approximation method, an F test method, and a log likelihood method. Each of these methods are applied to three case studies. One case study is a problem with synthetic data, and the other two case studies identify hydraulic parameters in groundwater flow problems based on experimental well test results. The confidence regions for each case study are analyzed and compared. Although the F test and log likelihood methods result in similar regions, there are differences between these regions and the regions generated by the linear approximation method for nonlinear problems. The differing results, capabilities, and drawbacks of all three methods are discussed.