Letter: Impact on health and healthcare costs if monounsaturated fatty acids were substituted for conventional dietary oils in the United States
Bibliographic record
Abstract
The recent review article by Abdullah et al.1 presents an analysis of possible savings in medical spending in the United States if the population, or at least a section of the population, were persuaded to increase their intake of monounsaturated fatty acids (MUFAs) while reducing intake of other types of fat. The focus is on reducing the incidence of coronary heart disease (CHD) and type 2 diabetes. Unfortunately, the authors greatly exaggerate the potential savings generated from an increased intake of MUFAs. As CHD and diabetes both have a high mortality rate, the prevention of these diseases would, therefore, lead to a significant reduction in mortality. However, the authors ignore the costs generated when people live longer. People who live longer will inevitably develop other diseases that are common in the elderly such as cataracts, dementia, cancer, and osteoporosis. These are expensive diseases to treat. Moreover, when people live longer, they will receive social security payments for more years. When these factors are taken into consideration, it is questionable whether any real savings would be achieved. It would be more accurate to say that costs are merely postponed. The above arguments have been made previously with respect to smoking.2 In 1 analysis, it was estimated that if smokers quit, this would lead to reduced healthcare costs in the short term but increased costs in the long term.3 This has also been demonstrated for obesity (ie, the prevention of obesity reduces healthcare spending in the short term but leads to increases in the long term).4
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".