Bibliographic record
Abstract
This symposium discussed several recent initiatives used around the world to improve the management of hypertensive patients and achieve better blood pressure (BP) control. The key objectives of the symposium were to review the current position with regards to BP control in Europe, to discuss the initiatives used in Italy, France, and Canada to improve hypertension management and their outcomes, and to assess how single-pill fixed-dose combinations of antihypertensive drugs have improved adherence. Some of the key barriers to BP control were discussed and measures to overcome these presented, so that further improvements in hypertension management can be achieved going forward. Prof Anthony Heagerty opened the meeting by discussing the key causes of suboptimal BP control and the results of the SPRINT study. Prof Massimo Volpe presented the initiative to achieve 70% BP control and assessed its success to date in Italy. Prof Jean-Jacques Mourad discussed the results of the PAssAGE 2014 study and French League Against Hypertension Survey (FLAHS) in 2015, following the initiative to achieve 70% BP control in France by the end of 2015. Prof Raj Padwal presented the Canadian hypertension Education Program (CHEP) and the improvements in the management of hypertensive patients in Canada. Finally, Dr Julian Segura bought the meeting to a close by discussing how fixed-dose combinations have improved adherence in clinical practice.
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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".