Bibliographic record
Abstract
OBJECTIVE: To identify patients who may or may not benefit from use of new drugs for weight loss and to aid in minimizing loss of lean mass through proactive nutrition and exercise interventions. QUALITY OF EVIDENCE: Choices and interventions are evaluated using the Grading of Recommendations Assessment, Development and Evaluation framework. Quality varies widely and is documented in multiple tables. MAIN MESSAGE: Semaglutide and tirzepatide should be used in patients living with obesity or with overweight accompanied by weight-related comorbidity. Long-term use may be necessary. Use in children and adolescents has proven effective for weight reduction, but long-term consequences are unknown. Use in elderly patients may be harmful. Because weight loss by any means is accompanied by loss of lean mass, specifically muscle and bone, particular attention must be paid to nutrition and exercise. Protein supplementation is effective to preserve muscle mass. Resistance training is effective in mitigation of both muscle and bone loss. Both resistance and aerobic training are beneficial in preventing osteopenia in weight loss, which may contribute to premature mortality. There is observational evidence that weight cycling may be harmful in that weight regain can be composed primarily of fat, multiple cycles of which may actually increase obesity in individuals. This is of particular concern because of the cost and limited availability of new weight loss drugs leading to large rates of discontinuation. Similarly, patients using drugs for small amounts of weight loss are likely to regain or overshoot if they discontinue. CONCLUSION: Patient selection for use of new anti-obesity drugs should match those included in clinical trials and be paired with dietary and exercise interventions used in those trials. Use at the extremes of age is problematic because of lack of long-term data. Intermittent use for small amounts of weight loss may be harmful. More ongoing data are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.027 |
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".