Show me the evidence to guide nutrition practice: Scoping review of macronutrient dietary treatments after metabolic and bariatric surgery
Bibliographic record
Abstract
BACKGROUND: Clinical practice recommendations for macronutrient intake in Metabolic and Bariatric Surgery (MBS) are insufficiently grounded in the research, possibly due to a paucity of research in key areas necessary to support macronutrient recommendations. An initial scoping review, prior to any systematic review, was determined to be vital. OBJECTIVES: To identify topical areas in macronutrients and MBS with a sufficient evidence base to guide nutrition recommendations. METHODS: PubMed, Cochrane, Ovid Medline, and Embase were initially searched in January 2019 (updated November 1, 2023) with terms encompassing current bariatric surgeries and macronutrients. Out of 757 records identified, 98 were included. A template was created. Five types of outcomes were identified for extraction: dietary intake, anthropometrics, adverse symptoms, health, and metabolic outcomes. All stages of screening and extraction were conducted independently by at least two authors and disagreements were resolved via team discussion. Macronutrient-related dietary treatments were classified as either innovative or standard of care. Descriptions of dietary arms were extracted in detail for a qualitatively generated typology of dietary or nutritional treatments. Heatmaps (treatments by outcomes) were produced to identify promising topics for further systematic analyses. RESULTS: We identified protein supplementation and "food-focused" (e.g., portion-controlled meals, particular foods in the diet, etc.) topical areas in MBS nutrition care with potentially sufficient evidence to create specific MBS Macronutrients guidelines and identified topical areas with little research. CONCLUSIONS: Clinical practice regarding macronutrient intake remains guided by consensus and indirect evidence. We detail ways that leadership at the profession level may remedy this.
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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 | 0.070 | 0.229 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.026 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".