Evaluation of Dietary Assessment Tools Used in Bariatric Population
Bibliographic record
Abstract
Severe obesity is associated with major health issues and bariatric surgery is still the only treatment to offer significant and durable weight loss. Assessment of dietary intakes is an important component of the bariatric surgery process. Objective: To document the dietary assessment tools that have been used with patients targeted for bariatric surgery and patients who had bariatric surgery and explore the extent to which these tools have been validated. Methods: A literature search was conducted to identify studies that used a dietary assessment tool with patients targeted for bariatric surgery or who had bariatric surgery. Results: 108 studies were included. Among all studies included, 27 used a dietary assessment tool that had been validated either as part of the study per se (n = 11) or in a previous study (n = 16). Every tool validated per se in the cited studies was validated among a bariatric population, while none of the tools validated in previous studies were validated in this population. Conclusion: Few studies in bariatric populations used a dietary assessment tool that had been validated in this population. Additional studies are needed to develop valid and robust dietary assessment tools to improve the quality of nutritional studies among bariatric patients.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".