The effect of weight loss on health‐related quality of life: systematic review and meta‐analysis of randomized trials
Bibliographic record
Abstract
The aim of this study was to examine the effect of weight loss on health-related quality of life (HRQL) in randomized controlled intervention trials (RCTs). MEDLINE, HealthStar and PsycINFO were searched. RCTs of any weight loss intervention and 20 HRQL instruments were examined. Contingency tables were constructed to examine the association between statistically significant weight loss and statistically significant HRQL improvement within five HRQL categories. In addition, Short Form-36 (SF-36) outcomes were pooled using random-effects models. Fifty-three trials were included. Seventeen studies reported statistically significant weight loss and HRQL improvement. No statistically significant associations between weight loss and HRQL improvement were found in any contingency table. Because of suboptimal endpoint reporting, quantitative data pooling could only be performed using 25% of SF-36 trials in any one model. Significant improvements in physical health were found: mean difference 2.83 points, 95% CI 0.55-5.1, for the physical component score, and mean difference 6.81 points, 95% CI 2.99-10.63, for the physical functioning domain score. Conversely, no significant improvements in mental health were found. No significant association was found between weight loss and overall HRQL improvement. Weight loss may be associated with modest improvements in physical, but not mental, health.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.019 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".