Prevalence and management of hypertension in primary care practices with electronic medical records: a report from the Canadian Primary Care Sentinel Surveillance Network
Bibliographic record
Abstract
BACKGROUND: Most epidemiologic reports on hypertension in Canada are based on data from surveys or on administrative data. We report on the prevalence and management of hypertension based on data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), which consists of validated, national, point-of-care data from primary care practices. METHODS: We included CPCSSN data as of Dec. 31, 2012, for patients 18 years and older who had at least 1 clinical encounter during the previous 2 years with one of the 444 family physicians and nurse-practitioners who participate in the CPCSSN. We calculated the prevalence of hypertension, the proportion of patients who achieved blood pressure targets, the number of encounters with primary care providers, comorbidities and pharmacologic management. RESULTS: Of the 250 346 patients who met the eligibility criteria, 57 180 (22.8%) had a diagnosis of hypertension. Of the 44 981 patients for whom blood pressure data were available, 35 094 (78.0%) had achieved both targets for systolic (≤□140 mm Hg) and diastolic (≤□90 mm Hg) pressure. Compared with patients who did not have a hypertension diagnosis, those with hypertension were significantly more likely to have a comorbidity and visited their primary care provider more often. Among the patients with hypertension, 12.1% were not taking antihypertensive medications; nearly two-thirds (61.7%) had their condition controlled with 1 or 2 drugs. INTERPRETATION: The prevalence of hypertension based on CPCSSN data was similar to estimates from the Canadian Health Measures Survey. Although achievement of blood pressure targets was high, patients with hypertension had more comorbidities and saw their primary care provider more often than those without hypertension.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".