Is the response to antihypertensive drugs heterogeneous? Rationale for personalized approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Arterial hypertension represents the most important cardiovascular risk factor with a direct responsibility for a large share of cardiovascular mortality and morbidity in the world. Despite the wide availability of antihypertensive therapies with documented effectiveness, blood pressure control still remains largely unsatisfactory in large segments of the population. Guidelines for the management of arterial hypertension suggest the preferential use of five classes of drugs-angiotensin-converting enzyme inhibitors, angiotensin II type I receptor inhibitors, calcium channel blockers, thiazide/thiazide-like diuretics, and beta-blockers-recommending the use of combination therapy, preferably in pre-established combinations, for the majority of hypertensive patients. The evidence of a non-negligible heterogeneity in the response to different antihypertensive drugs in different patients suggests the opportunity for personalization of treatment. The notable phenotypic heterogeneity of the population of hypertensive patients in terms of genetic structure, behavioural aspects, exposure to environmental factors, and disease history imposes the need to consider all the potential determinants of the response to a specific pharmacological treatment. The progressive digitalization of healthcare systems is making enormous quantities of data available for machine learning systems which will allow the development of management algorithms for truly personalized antihypertensive therapy in the near future.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it