Missing the Target: A Scoping Review of the Use of Percent Weight Loss for Obesity Management
Bibliographic record
Abstract
INTRODUCTION: To co-create comprehensive targets for obesity management, we need to understand the genesis and current use of percent weight loss targets in research. The goals of our scoping review are to (1) synthesize the literature on percent weight loss targets for adults with obesity and (2) discuss the percent weight loss targets in context with their health benefits. METHODS: We searched Cochrane, MEDLINE, and EMBASE for English language, pharmaceutical, and/or behavioral intervention studies in adults with obesity where the explicit aim of the study was weight reduction defined as a percent of body weight. Reviewers screened citations and extracted data including study characteristics. RESULTS: From 16,164 abstracts, we included 30 citations which were mostly randomized controlled trials (RCTs) (n = 17) or quasi-experimental studies (n = 12) published between 1992 and 2024. Most of the studies had target weight loss goals between 3% and 10% of body weight (n = 28), while n = 2 had body weight loss goals of 15% or 30%. The proportion of participants who met the percent weight loss target ranged from 5.9% (nutrition only study) to 85% (pharmaceutical study). The studies reported different reasons for targeting a percentage of weight loss such as disease-specific outcomes, reduced risk of disease, or patient-reported outcomes. CONCLUSION: Percent weight loss targets were based on similar research and were often not feasible nor sustainable for most participants. The design of these interventions and evaluation of obesity management would benefit from more patient-focused parameters which could help to co-design comprehensive targets for research and practice.
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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 | 0.076 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".