Absolute height-specific thresholds to identify elevated blood pressure in children
Bibliographic record
Abstract
OBJECTIVE: Identification of children with elevated blood pressure (BP) is difficult because of the multiple sex, age, and height-specific thresholds to define elevated BP. We propose a simple set of absolute height-specific BP thresholds and evaluate their performance to identify children with elevated BP in two different populations. METHODS: Using the 95th sex, age, and relative-height BP US thresholds to define elevated BP in children (standard criteria), we derived a set of (non sex- and non age-specific) absolute height-specific BP thresholds for 11 height categories by 10 cm increments. Using data from large school-based surveys conducted in Switzerland (N = 5207; 2621 boys, 2586 girls; age range: 10.1-14.9 years) and in the Seychelles (N = 25 759; 13 048 boys, 12 711 girls; age range: 4.4-18.8 years), we evaluated the performance of these height-specific thresholds to identify children with elevated BP. We also derived sex-specific absolute height-specific BP thresholds and compared their performance. RESULTS: In the Swiss and the Seychelles surveys, the prevalence of elevated BP (standard criteria) was 11.4 and 9.1%, respectively. The height-specific thresholds to identify elevated BP had a sensitivity of 80 and 84%, a specificity of 99 and 99%, a positive predictive value of 92 and 91%, and a negative predictive value of 97 and 98%, respectively. Performance of sex-specific absolute height-specific BP thresholds was similar. CONCLUSION: A simple table of height-specific BP thresholds allowed identifying children with elevated BP with high sensitivity and excellent specificity.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".