Managing hypertension in general practice: a cross-sectional study of treatment and ethnicity
Bibliographic record
Abstract
BACKGROUND: NICE guidelines are the accepted standard for determining the management of hypertension in UK primary care. AIM: To explore adherence and non-adherence to NICE hypertension guidelines, the extent to which this influences blood pressure control, and the role of ethnicity. DESIGN AND SETTING: A cross-sectional study was conducted based on primary care data from Lambeth DataNet, a database of primary care records in one inner-city London borough. METHOD: NICE guidelines were used to determine adherence to recommended treatment options for four groups of patients with hypertension: aged <55 years on monotherapy; aged ≥55 years on monotherapy; any age on dual therapy; any age and with comorbid diabetes. Blood pressure control was determined for each treatment category and ethnic group. The study controlled for age, sex, social deprivation, and clustering within general practices. RESULTS: A total of 32 183 patients were identified with a current diagnosis of hypertension. Ethnic coding was available for 28 320 (88.0%). Overall, 13 546 patients with ethnicity coding could be allocated to one of the four clinical categories of hypertension; 44% of these patients received non-guideline-adherent treatment; ethnicity was not a significant determinant. Mean arterial pressure did not differ significantly between those receiving 'correct' or 'incorrect' hypotensive therapy. DISCUSSION: Evidence-based guidelines for the management of hypertension were not followed in a relatively large proportion of patients included in this study. Nevertheless, no evidence was found that failure to follow treatment recommendations resulted in poorer blood pressure control. Further work is needed to determine the reasons for non-implementation of guideline recommendations in primary care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".