Bibliographic record
Abstract
Hypertension is the most common condition managed in the primary care setting. It is a potent but modifiable risk factor for cardiovascular disease (CVD) and premature mortality. Currently, approximately 25% of Canadian adults have a diagnosis of hypertension. However, the global community is experiencing challenges with optimizing hypertension management; it is estimated that at least 23% of individuals globally have effectively managed hypertension. Furthermore, if clinicians were able to identify and adequately intervene in these cases, we could prevent 10.8 million deaths per year. This statistic is extremely relevant to Canadian practitioners as our most recent Canadian data suggest that 34% of adults with hypertension are not achieving target blood pressure (BP) due to undertreatment or lack of awareness. The undertreatment of hypertension is complex. There are a number of structural and environmental drivers of CV risk, and there is increasing recognition that if we are going to be effective at CV risk reduction, we must acknowledge the significant role that social determinants play in the development of risk factors including hypertension, smoking, obesity and diabetes. The clinical guidance concerning hypertension may inadvertently contribute to the challenges we are seeing globally with hypertension management. There are numerous guidelines available to inform clinicians about how to provide supportive care for individuals with hypertension, and discordance between guidelines and the granularity of guidelines make implementation challenging. While there is disparity between guidelines, as well as differences regarding hypertension nomenclature, there is unanimous agreement that accurate BP measurement, risk-based thresholds for intervention, simplified approaches to pharmacotherapy, and well-structured care are the foundations of effective hypertension management.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".