Current etiology of hypertension in European children – role of serum uric acid
Bibliographic record
Abstract
2. Abstract Background While hypertension (HT) in pediatric patients is often secondary (SH), recent trends show a rise in primary hypertension (PH), which is associated with an increasing global prevalence of obesity. Our study aimed to assess the etiology of HT and predictors of PH in a large European cohort of children referred for HT based on office blood pressure (BP) measurements. Methods We performed retrospective analysis of 2008 children aged 0–18 years (12.3 ± 4.9 years) diagnosed with HT. Patients were classified into white coat hypertension (WCH), PH, or SH groups based on office BP and 24-hour ambulatory BP monitoring (ABPM). Clinical, anthropometric, and biochemical data were collected to differentiate PH and SH and to identify predictors of PH. Results Out of 2008 patients included in the analysis, HT was confirmed in 1452 patients (556 were classified as WCH). Of 1452 patients with HT: 42.8% had PH, while 57.2% had SH, mainly secondary to renal parenchymal disease (33.2% of SH patients), post-kidney transplant HT (23.1%), aortic coarctation (15.9%) and renovascular HT (13.8%). However, PH started to be the dominant cause of HT after 13 years of age and was diagnosed in 59.1% of 13–18-year-old patients with confirmed HT. Age ≥ 13 years, obesity (BMI-SDS ≥1.65), and serum uric acid ≥ 5.5 mg/dL were identified as significant PH predictors. Conclusions Our study provides valuable insights into the current etiology of pediatric HT and highlights the role of uric acid level assessment in the diagnosis of PH in children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".