Greater Than Predicted Decrease in Resting Energy Expenditure and Weight Loss: Results From a Systematic Review
Bibliographic record
Abstract
Changes in resting energy expenditure (EE) during weight loss are said to be greater than what can be expected from changes of body mass, i.e., fat mass (FM) and fat-free mass (FFM) but controversy persists. The primary focus of this study was to investigate whether there is a greater than predicted decrease in resting EE during weight loss in a large sample size through a systematic review. The study data were weighted and a partial residual plot followed by a multiple regression analysis was performed to determine whether FM and FFM can predict the changes of resting EE after weight loss. Another subgroup of studies from which all necessary information was available was analyzed and compared against the Harris-Benedict (HB) prediction equation to determine whether the changes in resting EE were greater than what was expected. Subjects lost 9.4 ± 5.5 kg (P < 0.01) with a mean resting EE decline of 126.4 ± 78.1 kcal/day (P < 0.01). Changes in FM and FFM explained 76.5% and 79.3% of the variance seen in absolute resting EE at baseline and post-weight loss, respectively (P < 0.01). Analysis of the 1,450 subject subgroup indicated an ~29.1% greater than predicted decrease in resting EE when compared to the HB prediction equation (P < 0.01). This analysis does not support the notion of a greater than predicted decrease in resting EE after weight loss.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".