Bibliographic record
Abstract
Obesity is an established risk factor for numerous chronic diseases, and successful treatment will have an important impact on medical resources utilization, health care costs, and patient quality of life. With over 60% of our population being overweight, physicians face a major challenge in assisting patients in the process of weight loss and weight-loss maintenance. Low-calorie diets can lower total body weight by an average of 8% in the short term. These diets are well-tolerated and characterize successful strategies in maintaining significant weight loss over a 5-year period. Very-low-calorie diets produce a more rapid weight loss but should only be used for fewer than 16 weeks because of clinical adverse effects. Diets that are severely restricted in carbohydrates (3%-10% of total energy intake) and do not emphasize a reduction of energy intake may be effective in reducing weight in the short term, but there is no evidence that they are sustainable or innocuous in the long term because their high saturated-fat content may be atherogenic. Fat restriction in a weight-loss regimen is beneficial, but the optimal percentage has yet to be determined. Longitudinal trials are needed to resolve these issues. In this article I discuss the evidence for and pitfalls of various types of weight-loss diets and identify issues that physicians need to address in weight loss and weight-loss maintenance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".