Next Generation Weight Loss Drugs for the Prevention of Cancer?
Bibliographic record
Abstract
Background: Western populations are losing the battle over healthy weight management, and excess body weight is a notable cancer risk factor at the population level. There is ongoing interest in pharmacological interventions aimed at promoting weight loss, including GLP-1 receptor agonists (GLP-1RA), which may be a useful tool to stem the rising tide of obesity-related cancers. Purpose: To investigate the potential of next generation weight loss drugs (NGWLD) like GLP-1RA in population-level chemoprevention.Research Design: We used the OncoSim microsimulation tool to estimate the population-level reductions in obesity and the potentially avoidable obesity-related cancers in Canada over the next 25 years.Results: We estimated a total of 71 281 preventable cancers by 2049, with 36 235 and 35 046 cancers prevented for females and males, respectively. Among the 327 254 total projected cancer cases in 2049, 1.3% are estimated to be preventable through intervention with NGWLD.Conclusions: Pharmacologic intervention is not the ideal solution for the obesity-related cancer crisis. However, these agents and subsequent generations provide an additional tool to rapidly reduce body weight and adiposity in populations that have been extremely challenging to reduce weight with standard diet and exercise approaches. Additional research is needed around approaches to prevent initial weight gain and maintain long-term weight loss.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".