Obesity‐ and diet‐induced plasticity in systems that control eating and energy balance
Bibliographic record
Abstract
In April 2023, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), in partnership with the National Institute of Child Health and Human Development, the National Institute on Aging, and the Office of Behavioral and Social Sciences Research, hosted a 2-day online workshop to discuss neural plasticity in energy homeostasis and obesity. The goal was to provide a broad view of current knowledge while identifying research questions and challenges regarding neural systems that control food intake and energy balance. This review includes highlights from the meeting and is intended both to introduce unfamiliar audiences with concepts central to energy homeostasis, feeding, and obesity and to highlight up-and-coming research in these areas that may be of special interest to those with a background in these fields. The overarching theme of this review addresses plasticity within the central and peripheral nervous systems that regulates and influences eating, emphasizing distinctions between healthy and disease states. This is by no means a comprehensive review because this is a broad and rapidly developing area. However, we have pointed out relevant reviews and primary articles throughout, as well as gaps in current understanding and opportunities for developments in the field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".