Hypertension screening and follow-up in children and adolescents in a Canadian primary care population sample: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Uncertainty exists about the need to screen for hypertension in children and adolescents. Information on current screening and follow-up rates in Canadian community practices is not available. There are no Canadian guidelines on the subject. We sought to identify current rates of pediatric hypertension screening and follow-up in Canada. In addition, we examined patient and provider characteristics associated with rates of blood pressure screening. METHODS: We used electronic medical record data extracted on Apr. 1, 2013, from 79 family practices in Toronto. We identified children seen at least twice between the ages of 3 and 18 years, with at least 6 months between first and last encounter. We used Multivariate Poisson regression analysis to analyze variation in blood pressure measurement rates and associations with patient and physician factors. RESULTS: We identified 5996 children (62% of 9667 in total) who had at least 1 blood pressure measurement recorded. Of these children, 14% had at least 1 abnormal blood pressure measurement, and of those children, only 5% had a follow-up measurement recorded within 6 months. After adjustment, increases in rates of blood pressure measurements were associated with greater number of encounters (rate ratio [RR] = 1.03, 95% confidence interval [CI] 1.02-1.04, p < 0.001), older age at first encounter (RR = 1.06, 95% CI 1.03-1.10, p = 0.002), and female sex (RR = 1.12, 95% CI 1.03-1.20, p = 0.006). Obesity or a recorded family history of hypertension were not associated with more blood pressure measurements. Female physicians recorded more blood pressure measurements than did male physicians (RR = 1.41, 95% CI 1.04-1.89, p = 0.02). INTERPRETATION: This screening measure was frequently done and appeared to be incompletely followed up. Clear guidance is needed; guideline developers should consider reviewing this topic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".