Examining Dietary Clusters in Candidates for Metabolic-Bariatric Surgery and their Association to Metabolic Status
Bibliographic record
Abstract
Candidates for metabolic-bariatric surgery (MBS) have a unique nutritional status profile; clustering their macronutrients and micronutrients dietary intakes may present inter-individual differences. This study aimed to: (1) describe the macro- and micro- nutrient intake patterns in candidates for a MBS; and (2) assess the associations between these patterns and metabolic status (body fat %, HbA1c, lipid profile, granulocytes (GR), international normalised ratio, C- Reactive Proteins). Three-day dietary data from a mobile application and metabolic markers from a blood draw were collected 3 months pre-MBS from a study conducted in Quebec, Canada. Participants’ (N=30) mean age was 45.50 ± 9.83 years and BMI was 46.03 ± 7.61 kg/m2. Using the FASTCLUS procedure, a high sugar/high caloric diet (Cluster 1), high protein/high cholesterol diet (Cluster 2), and a low fiber/low saturated fat diet (Cluster 3) were observed. Analyses demonstrated significantly greater low-density lipoproteins (LDL) (5.28 ± 0.71 mmol/L) and GR (5.64 ± 0.21 109/L) in Cluster 1 relative to Clusters 2 (LDL: 2.38 ± 0.28 mmol/L; p= 0.0130), (GR: 4.71 ± 0.09 109/L; p= 0.003) and 3 (LDL: 1.76 ± 0.44 mmol/L; p = 0.0097), (GR: 5.01 ± 0.15 109/L; p= 0.015). Findings can inform variability in nutrient distribution in candidates for MBS. Future studies should compare these clusters to a control group or post-surgery dietary clusters and metabolic status.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".