Difficulties of Introducing the National Service Framework for Heart Failure Into General Practice in the UK
Bibliographic record
Abstract
BACKGROUND: The National Service Framework (NSF) sets standards for the management of heart failure in the UK. Loop diuretics are commonly first prescribed in primary care. Some patients taking these drugs have heart failure and may benefit from other treatments including ACE inhibitors. Accurate diagnosis in primary care is essential for the aims of the NSF to be realised. AIMS: To investigate loop diuretic prescribing in general practice, to analyse recorded clinical features, patient investigations and ACE inhibitor use in this population. METHOD: One thousand three hundred and one patients taking loop diuretics were identified from prescription records of seven general practices. Demographic details, clinical features, investigations and drug treatments were extracted from patient records. RESULTS: The prevalence of loop diuretic prescribing increased with age. Twenty percent of patients were attributed a diagnosis of heart failure but relevant clinical features were recorded in less than 50% of patient records. Open access echocardiography was used in 8.9% of patients. ACE inhibitors were prescribed in 39.8% of patients considered to have heart failure. 18.2% of these were taking the recommended target dose. CONCLUSION: Loop diuretics are prescribed commonly, particularly in the elderly. There is no clear pattern of documented clinical features that leads to prescription of these drugs. Open access echocardiography is rarely used to aid diagnosis. ACE inhibitors are under-prescribed and under-dosed in patients diagnosed with heart failure in this study population.
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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 | 0.075 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".