The Diagnosis of Heart Failure in General Practice: Implications for the UK National Service Framework
Bibliographic record
Abstract
BACKGROUND: The UK National Service Framework recommends patients with suspected heart failure undergo echocardiography. Selection of patients for this investigation in primary care is difficult. It is not clear which clinical features best identify patients with left ventricular systolic dysfunction. AIM: Using echocardiography, to establish the accuracy of primary care diagnosis of left ventricular systolic dysfunction. To investigate the sensitivity, specificity and predictive values of clinical features in the diagnosis of left ventricular systolic dysfunction. STUDY: A cross-sectional study of 621 patients from a population prescribed loop diuretics in 7 general practices. METHOD: Clinical diagnoses were extracted from general practice records. Symptoms, clinical signs, ECG features, brain natriuretic peptide levels and echocardiographic findings were studied in a research clinic. RESULTS: Left ventricular systolic dysfunction (ejection fraction <40%) was present in 50% of 621 patients prescribed loop diuretics in primary care. General practice diagnoses showed high false positive rates. Individual or combinations of clinical features did not accurately predict left ventricular systolic dysfunction. CONCLUSION: These results suggest the clinical diagnosis of left ventricular systolic dysfunction is inaccurate in this population. General practitioners should have a low threshold for referring patients prescribed loop diuretics for echocardiography. Increased open access echocardiography facilities will be needed.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".