Trends of paediatric hypertension screening and management in primary care before and during the coronavirus disease 2019 pandemic: A retrospective cohort study
Bibliographic record
Abstract
Objectives: We assessed trends in primary care paediatric blood pressure (BP) screening, follow-up, and treatment before and during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Retrospective cohort study using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network to capture paediatric visits (aged 3 to 18) between January 1, 2011, and December 31, 2020. Time-series analysis was performed using documentation of monthly BP, high BP, follow-up of abnormal BP, and antihypertensive prescribing. We assessed differences between pre (January 1, 2011 to March 11, 2020) and during COVID-19 (March 12, 2020 to December 31, 2020). Results: Of 343,191 paediatric patients, 30.9% had ≥1 paediatric BP documented. Documentation of BP increased each year from 17.3% in 2011 to 19.8% in 2019 (β = 0.05, 95% CI 0.04, 0.07, P < 0.001), with a decrease in trend in 2020 to 11.0% (β = -16.95, 95% CI -18.91, -14.99, P < 0.001). There was an increasing pre-pandemic trend for laboratory screening and prescribing (β = 0.12, 95% CI 0.1, 0.14, P < 0.0001; β = 0.02, 95% CI 0.02, 0.02, P < 0.0001). During the COVID-19 pandemic, laboratory screening further increased (24.5% to 31.1%; β = 5.19, 95% CI 2.03, 8.35, P = 0.002), whereas there was no significant change in prescribing trends (1.3% to 1.4%; β = 0.15, 95% CI -0.01, 0.32, P = 0.07). Conclusions: Documentation of BP increased annually, then declined precipitously during the COVID-19 pandemic. Despite lower BP screening and follow-up, the prevalence of hypertension and antihypertensive prescribing remained stable. Clinical practice trends in primary care highlight areas to improve the care and management of hypertensive paediatric patients.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".