High-fidelity porcine models of metabolic syndrome: a contemporary synthesis
Bibliographic record
Abstract
This review aims to describe and compare porcine models of metabolic syndrome. This syndrome and its associated secondary comorbidities are set to become the greatest challenge to healthcare providers and policy makers in the coming century. However, an incomplete understanding of the pathogenesis has left significant knowledge gaps in terms of efficacious therapeutics. To further our comprehension and, in turn, management of metabolic syndrome, appropriate high-fidelity models of the disease complex are of great importance. In this context, our review aims to assess the most promising porcine models of metabolic syndrome currently available for their similarity to the human phenotype. In addition, we aim to highlight the strengths and shortcomings of each model in an attempt to identify the most appropriate application of each. Although no porcine model perfectly recapitulates the human metabolic syndrome, several pose satisfactory approximations. The Ossabaw miniature swine in particular represents a highly translatable model that develops each of the core parameters of the syndrome with many of the associated secondary comorbidities. Future high-fidelity porcine models of metabolic syndrome need to focus on secondary sequelae replication, which may require extended induction period to reveal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".