Metabolic syndrome: signs and symptoms running together
Bibliographic record
Abstract
Children with kidney disease are at increased risk of having several comorbidities such as obesity, dyslipidemia, hypertension, and impaired glucose tolerance, and patients with a constellation of these symptoms are considered to have the MS. Children with kidney disease, and ESRD in particular, are at increased CV risk, as are patients with the MS. To determine the impact MS has on a particularly vulnerable population of children, those who have received a kidney transplant, Wilson et al. explored the prevalence of MS and the association of MS with cardiac abnormalities among this subset of children. They found an overall high prevalence of MS among pediatric transplant recipients and that the risk of left ventricular hypertrophy was higher among children with MS after renal transplant compared to those without MS. Review of the most common definitions of MS and also the clinical implications are discussed. While there is no doubt that children with kidney disease have a high prevalence of CV risk factors and that these children are at risk for CV events early in life, whether the sum of the parts of MS confers increased risk over what is seen with individual risk factors that often run together remains to be seen.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".