TITLE: Use of Diuretics for Hypertension in Patients with Reduced Renal Function: A Review of Clinical Effectiveness, Safety, and Guidelines
Bibliographic record
Abstract
Hypertension affects 20 % of the population world-wide and an estimated 5 million Canadians. 1,2 Risk factors for hypertension include advanced age, male gender, genetics, diet, lack of physical activity, obesity, social characteristics, and ethnicity. 1 While hypertension is often an asymptomatic disease from the patient perspective, the consequences of uncontrolled blood pressure are manifested by target organ damage, examples of which include stroke, cardiovascular disease, end-stage renal disease, vascular dementia, and retinopathy. 1 Thiazide diuretics are one of the many classes of anti-hypertensive agents that are recognized as first-line therapies for blood pressure lowering according to Canadian 2 and international guidelines. 3-5 Thiazide diuretics initially exert their anti-hypertensive effects by promoting the renal excretion of sodium and fluid; however, other mechanisms have been proposed to explain their effects longer-term. 6 Hydrochlorothiazide, chlorthalidone, or indapamide are examples of thiazide or thiazide-like diuretics available on the Canadian market as single agents or in combination with other anti-hypertensive agents such as beta-blockers and angiotension converting enzyme inhibitors. 7-10 Product monographs 7-10 and other literature 11,12 suggest that thiazide diuretics are largely
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".