Gastric modulation of food reward, olfaction and taste in obesity and bariatric surgery: an artificial intelligence assisted scoping review protocol
Bibliographic record
Abstract
Abstract Objective To understand the extent and nature of the available research on gastric modulation of food reward, olfaction, and taste in people with obesity or those who have undergone bariatric surgery. Introduction Bariatric surgery-induced weight loss is partially attributed to shifts in food preferences resulting from alterations in sensory perceptions and changes in reward system. The stomach’s innervation and mechanical function have been theorized to play a significant role in these modifications, as suggested by numerous preclinical studies. However, the extent and nature of these connections in clinical settings require further elucidation. Inclusion criteria This review will examine studies on the influence of gastric innervation and/or mechanical function on food reward, olfaction, and taste. Selected studies will include participants of all ages with obesity or bariatric surgery. Both observational studies and controlled experiments will be considered, while study protocols, opinion articles, letters to the editor, book chapters, oral communication or poster abstracts and systematic reviews will be excluded. Methods The search will be undertaken in MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, Google Scholar, and gray literature. No date parameters will be set, and all languages will be considered. Citations will be uploaded into EndNote 20.0 and duplicates removed using Covidence. The remaining studies will be analyzed by 3 reviewers using a two-stage procedure with the ASReview python package. The full-text screening and the data extraction will be conducted by 2 reviewers on Covidence. An additional reviewer will be consulted in the event of disagreement. Tabulated results will be accompanied by a narrative summary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.103 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.010 |
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".