Design and methodology of POWER, an open‐label observation of the effect of primary care interventions on total cardiovascular risk in patients with hypertension
Bibliographic record
Abstract
This article describes the design and methodology of the POWER study (Physicians' Observational Work on Patient Education According to their Vascular Risk). POWER is an open-label multinational postmarketing study of the angiotensin II-receptor blocker eprosartan. The Systemic Coronary Risk Evaluation (SCORE) model has been used to estimate total cardiovascular risk and changes in total cardiovascular risk status during treatment for patients recruited in all countries other than Canada. Framingham Heart Study equations have been used to estimate risk in the Canadian contingent of POWER. Observations from POWER will provide insights into how clinicians try to achieve blood pressure goals within the framework of total cardiovascular risk management and how they integrate their treatment of blood pressure with other interventions. Experience during the POWER study may also help to affirm the utility, practicability and perhaps limitations of the SCORE system for estimating total cardiovascular risk and identify ways to improve the acceptance and implementation of risk estimation methods in cardiovascular primary prevention.
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 | 0.110 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".