Primary care prescribing patterns in Ireland after the publication of large hypertension trials
Bibliographic record
Abstract
What is already known about this subject • Between 2000 and 2004, three large hypertension trials were particularly important in choice of antihypertensive drug; LIFE, ALLHAT and VALUE. • Studies in the US and Canada have shown that subsequent prescribing patterns have been influenced through such publications. What this study adds • There was little or no effect of any of the trials on new antihypertensive prescribing in Ireland, except for a slight increase in new prescriptions for ACE inhibitors following ALLHAT, and calcium channel blockers following the VALUE trial. Aims This study assessed prescribing patterns of antihypertensive therapies (AHT) before and after the publication of the LIFE, ALLHAT and VALUE trials between 2000 and 2005. Methods The Irish HSE‐PCRS prescribing database was used to identify those initiated any AHT. Any change 12 months before and after the trial publications was examined using a segmented regression analysis. Results There was little or no effect of any of the trials on new AHT prescribing, except for ALLHAT where there was an increase in new prescriptions for ACE inhibitors, and VALUE with a slight increase in prescriptions for calcium channel blockers. Conclusions Our findings show that there was little or no effect of any of the three clinical trials studied on new AHT prescribing patterns in Irish general practice. Future studies should assess any underlying barriers to implementing new evidence into 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 | 0.022 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".