Drug management for hypertension in type 2 diabetes in family practice.
Bibliographic record
Abstract
OBJECTIVE: To describe the number and classes of antihypertensive medications prescribed to patients with type 2 diabetes in community family practices, and to estimate the aggressiveness or "dosage intensity" of prescribing for hypertension in these situations. DESIGN: Practice-based, cross-sectional observational study. SETTING: Seventeen rural and urban family practices in the Maritime Family Practice Research Network in Nova Scotia, New Brunswick, and Prince Edward Island. PARTICIPANTS: A total of 670 patients with type 2 diabetes, ranging from 25 to 92 years of age. MAIN OUTCOME MEASURES: Number, classes, and combinations of classes of antihypertensive medications prescribed, as well as an index of each medication's dosage intensity. RESULTS: Almost 80% of patients studied had hypertension. Participants with hypertension were taking an average of 2.5 medications, and 47.6% were taking 3 or more antihypertensive medications, but only 27.1% reached target blood pressure values of less than 130/80 mm Hg. Older patients took more antihypertensive medications, but there were no differences by sex. More than 90% were taking angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, 66% were taking diuretics, 41% were taking beta-blockers, and 38% were taking calcium channel blockers. We cannot describe the sequence in which antihypertensive medication classes were added, but analysis of patients taking multiple drug classes suggests that most patients were started on angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, followed by diuretics, beta-blockers, or calcium channel blockers. The most commonly used medications were prescribed at higher than two-thirds the maximum dose effective for hypertension. CONCLUSION: Hypertension is very common among family practice patients with type 2 diabetes; of those patients, few reach target blood pressures. Practice-based strategies to increase dosing and number of medications prescribed might be required.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".