Bibliographic record
Abstract
Dear Editor-in-Chief: Bassett et al. (1) studied an Old Order Amish community in Ontario and found far lower prevalences of overweight and obesity than those in the general Canadian and U.S. populations. Having studied another group of Old Order Amish living in Lancaster County, Pennsylvania, we would like to supplement with some of our own, contrasting observations. While the cohort studied by Bassett et al. was relatively young (46% was less than 30 yr old), our cohort (N = 953) was aged 45–70 yr. Somewhat surprisingly, given the physically active Amish lifestyle, mean BMI was slightly higher in the Lancaster Amish than in the contemporary National Health and Nutrition Examination Study (NHANES) participants of European extraction and comparable age (BMI 27.9 ± 4.6 kg·m−2 vs 27.0 ± 5.1 kg·m−2) (4). Oral glucose tolerance tests revealed that the proportion of Amish subjects who had dysregulated glucose homeostasis (impaired glucose tolerance or diabetes) was about the same as in the mainstream population. However, the prevalence of frank diabetes was surprisingly only about half of that in the mainstream population. This observation was consistent across all age strata studied (3). While genetic founder effects cannot be excluded, our findings strongly suggest that independent of BMI, the Amish environment may provide protection against diabetes. Possible explanations are high levels of physical activity and a relatively delayed accrual of body fat among the Amish (our own casual observations, including an apparent absence of obesity in children, are consistent with Bassett's reported low prevalence of obesity in younger Amish). Thus, the Lancaster Amish may illustrate the “Fat and Fit” paradigm described by Blair (2). However, as nonfarming occupations are becoming increasingly common among the Lancaster Amish, levels of physical activity are likely to vary. A study is now underway to determine the metabolic and cardiovascular correlates of physical activity levels among the Lancaster Amish. Soren Snitker, M.D., Ph.D. Alan R. Shuldiner, M.D. University of Maryland School of Medicine, Baltimore, MD
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".