A Simplified Approach to the Treatment of Uncomplicated Hypertension
Bibliographic record
Abstract
Notwithstanding the availability of antihypertensive drugs and practice guidelines, blood pressure control remains suboptimal. The complexity of current treatment guidelines may contribute to this problem. To determine whether a simplified treatment algorithm is more effective than guideline-based management, we studied 45 family practices in southwestern Ontario, Canada, using a cluster randomization trial comparing the simplified treatment algorithm with the Canadian Hypertension Education Program guidelines. The simplified treatment algorithm consisted of the following: (1) initial therapy with a low-dose angiotensin-converting enzyme inhibitor/diuretic or angiotensin receptor blocker/diuretic combination; (2) up-titration of combination therapy to the highest dose; (3) addition of a calcium channel blocker and up-titration; and (4) addition of a non-first-line antihypertensive agent. The proportion of patients treated to target blood pressure (systolic blood pressure <140 mm Hg and diastolic blood pressure <90 mm Hg for patients without diabetes mellitus or systolic blood pressure <130 mm Hg and diastolic blood pressure <80 mm Hg for diabetic patients) at 6 months was analyzed at the practice level. The proportion of patients achieving target was significantly higher in the intervention group (64.7% versus 52.7%; absolute difference: 12.0%; 95% CI: 1.5% to 22.4%; P=0.026). Multivariate analysis of patient-level data showed that assignment to the intervention arm increased the chance of reaching the target by 20% (P=0.028), when adjusted for other covariates. In conclusion, the Simplified Treatment Intervention to Control Hypertension Study indicates that a simplified antihypertensive algorithm using initial low-dose fixed-dose combination therapy is superior to guideline-based practice for the management of hypertension.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".