Liver function tests in patients with hypertension in primary care: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Liver function tests (LFTs) are frequently used to monitor patients with hypertension in UK primary care. Evidence is lacking on whether testing improves outcomes. AIM: To estimate the diagnostic accuracy of LFTs in patients with hypertension and determine downstream consequences of testing. DESIGN & SETTING: Prospective study using the Clinical Practice Research Datalink (CPRD). METHOD: In total, 30 000 patients with hypertension who had LFTs in 2015 were randomly selected from CPRD. The diagnostic accuracy measures for eight LFT analytes and an overall LFT panel were calculated against the reference standard of liver disease. Rates of consultations, blood tests, and referrals within 6 months following testing were measured. RESULTS: The 1-year incidence of liver disease in patients with hypertension was 0.5% (95% confidence interval [CI] = 0.4% to 0.6%). Sensitivity and specificity of an LFT panel were modest: 61.3% (95% CI = 53.1% to 69.0%) and 73.8% (95% CI = 73.1% to 74.3%), respectively. The positive predictive value (PPV) of the eight individual LFT analytes were low ranging from 0.2% to 8.9%. Among patients who did not develop liver disease, mean number of consultations, referrals, and tests were higher in the 6 months following false-positives at 10.5, 0.7 and 29.8, respectively, compared with true-negatives: 8.6, 0.6, and 19.8. CONCLUSION: PPVs of LFTs in primary care were low, with high rates of false-positive results and increased rates of subsequent consultations, referrals, and blood testing. Avoiding LFTs for routine monitoring could potentially reduce patients' anxiety, GP workload, and healthcare costs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".