A systematic review and meta‐analyses of food preference modifications after bariatric surgery
Bibliographic record
Abstract
Summary This systematic review and meta‐analyses aimed to synthesize evidence of the link between bariatric surgery and changes in food preferences, considering the method of assessment. MEDLINE, Cochrane Library, Web of Science, Cinahl, PsychINFO, ProQuest, and Open grey were searched incorporating two blocks of terms (“Intervention” and “Food Preferences”). Interventional or observational studies involving patients (BMI ≥ 35 kg m−2) with sleeve gastrectomy (SG) or Roux‐en‐Y Gastric Bypass (RYGB) and a control group were included. Meta‐analyses were performed comparing the standardized daily mean percentage energy from proteins, carbohydrates, and lipids between preoperative and postoperative patients. Fifty‐seven studies concerning 2,271 patients with RYGB and 903 patients with SG met the inclusion criteria, of which 24 were eligible for meta‐analysis. Despite a total reduction in macronutrient intakes, the meta‐analyses revealed a postoperative increase in percentage energy from proteins at 12 months (0.24, 95% CI: 0.03, 0.46, {I2} = 73%) and a decrease in percentage energy from fat at 1 month (−0.47, 95% CI: 0.86, 0.09, {I2} = 72%), up to 24 months (−0.20, 95% CI: −0.31, 0.08, {I2} = 0%). In conclusion, the present systematic review and meta‐analyses showed changes of food preferences in terms of macronutrient, food selection and, overall food appreciation up to 5 years following bariatric surgery.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.006 | 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) | 0.003 | 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".