Image3_Osteocalcin serum concentrations and markers of energetic metabolism in pediatric patients. Systematic review and metanalysis.pdf
Bibliographic record
Abstract
Background<p>Osteocalcin plays a role in glucose metabolism in mice, but its relevance in human energetic metabolism is controversial. Its relationship with markers of energetic metabolism in the pediatric population has not been systematically addressed in infants and adolescents.</p>Objective<p>This study aims to assess the mean differences between tOC, ucOC, and cOC among healthy children and children with type 1 or type 2 diabetes (T1D or T2D) and the correlation of these bone molecules with metabolic markers.</p>Methods<p>A systematic review and metanalysis were performed following PRISMA criteria to identify relevant observational studies published in English and Spanish using PubMed, Scopus, EBSCO, and Web of Science databases. The risk of bias was assessed using New Castle–Ottawa scale. Effect size measures comprised standardized mean difference (SMD) and Pearson correlations. Heterogeneity and meta-regressions were performed.</p>Results<p>The 20 studies included were of high quality and comprised 3,000 pediatric patients who underwent tOC, cOC, or ucOC measurements. Among healthy subjects, there was a positive correlation of ucOC with WC and weight, a positive correlation of tOC with FPG, HDL-c, WC, height, and weight, and a negative correlation between tOC and HbA1c. Among diabetic subjects, a negative correlation of ucOC with HbA1c and glycemia in both T1D and T2D was found and a negative correlation between tOC and HbA1c in T1D but not in T2D. The ucOC concentrations were lower in T2D, T1D, and patients with abnormal glucose status than among controls. The serum concentrations of tOC concentrations were lower among T1D than in controls. The patient's age, altitude, and HbA1c influenced the levels of serum tOC.</p>Conclusion<p>Osteocalcin is involved in energy metabolism in pediatric subjects because it is consistently related to metabolic and anthropometric parameters.</p>Systematic Review Registration<p>https://www.crd.york.ac.uk/prospero/, identifier: CRD42019138283.</p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.242 | 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".