Genotype-specific weight loss treatment advice: how close are we?
Bibliographic record
Abstract
Obesity, whose prevalence is continually rising, is one of the world's greatest health care burdens. This multifactorial condition is associated with many obesity-related conditions, such as type 2 diabetes, dyslipidemia, and cardiovascular disease. Weight loss is a significant challenge facing those wishing to reduce their disease risk. Of course, like obesity itself, weight loss is a complex phenomenon dependent on many environmental and genetic influences, and thus individual responses to weight loss interventions are incredibly variable. Currently, there are 3 major interventions used to reduce weight: diet, exercise, and pharmacotherapy. The findings from studies examining gene-diet (nutrigenetic), gene-exercise (actigenetic), and gene-pharmaceutical (pharmacogenetic) interactions, although not clinically applicable at this time, are gaining awareness. This review article summarizes the current evidence to support the contribution of DNA sequence variation in genes related to energy balance (expenditure and intake) in the response to weight loss intervention. There is no doubt that replication using more rigorous study designs that include the study of interactions between multiple genes and interventions is required to move towards the development of genotype-specific weight loss treatment strategies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".