Arterial HTN in children with T1DM-frequent and not easy to diagnose
Bibliographic record
Abstract
INTRODUCTION: To evaluate the diagnostic efficacy of the office blood pressure (OBP) and ambulatory blood pressure monitoring (ABPM) in the assessment of hypertension (HTN) in children with diabetes mellitus type 1 (T1DM). METHODS: We analyzed OBP and ABPM measurements in 84 diabetic children (43 boys) obtained at a median age of 14.9 yr and 6.3 +/- 3.5 yr after diagnosis of T1DM. OBP and ABPM results were converted into standard deviation scores (SDS). In addition, we analyzed blood pressure loads and nighttime dipping. The comparison between OBP and ABPM was performed using kappa coefficient and receiver operator curve (ROC). RESULTS: HTN was diagnosed in 43/84 (51%) patients using OBP (>95th percentile), and in 24/84 (29%) patients using ABPM ( > or = 95th percentile during 24 h, day or night). Both methods were in agreement in 33 ABPM normotensive and 16 ABPM hypertensive patients (most had nighttime HTN); 32% patients had white-coat HTN and 9.5% patients had masked HTN. The kappa coefficient was 0.175 (95% CI from -0.034 to 0.384) suggesting poor agreement between OBP and ABPM. Diastolic OBP was a better predictor of ABPM HTN (ROC area under the curve (AUC) = 0.71 +/- 0.06) than systolic OBP (AUC = 0.58 +/- 0.07). The percentage of non-dippers ranged from 7 to 23% in ABPM normotensive patients, and 21-42% in ABPM hypertensive patients who also had significantly higher BP loads (p < 0.0001). CONCLUSION: Children with T1DM often suffer from nocturnal, white coat- and masked HTN, which can not be assessed and predicted by the OBP.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".