A paradigm shift is also required in treatment goals and in performance indicators: reply
Bibliographic record
Abstract
Dear Sir,We totally agree that there is much more that can be performed to improve health through the control of hypertension; and that the requirement for a major paradigm shift in treatment goals and in performance indicators so as to ensure that control of hypertension does indeed improve health. Our population-based study of drug utilization patterns in a Canadian setting suggests an association with a significant benefit linked with a good adherence to antihypertensive medication and chronic heart failure in the context of primary prevention. We are convinced based on our population-based study that the adherence level to medication is a major gap to be fulfilled, giving that, the mean high adherence level to AH agents was around to 98% during the first year and 96% after 1 year of follow-up compared with 60% and 59.7% for the low adherence level, respectively. Thus, we suggest in addition to the requirement for a major shift in treatment goals and in performance indicators that an assessment of medication adherence should be incorporated into routine clinical practice to ensure a health improvement. A better adherence to pharmacological therapy is one of key factors in determining the success of various therapeutic approaches. Consequently, greater attention should also be paid to this aspect to improve patient outcomes. None declared.
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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 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.056 |
| Insufficient payload (model declined to judge) | 0.004 | 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".