Oral glucocorticoids and incidence of hypertension in people with chronic inflammatory diseases: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Only a few population-based studies have examined the association between glucocorticoids and hypertension, with inconsistent results. We aimed to investigate the effect of oral glucocorticoids on incidence of hypertension in adults with chronic inflammatory diseases. METHODS: We analyzed electronic health records from 389 practices in England during 1998-2017 of adults diagnosed with any of 6 chronic inflammatory diseases but with no previous diagnosis of hypertension. We used glucocorticoid prescription data to construct time-variant daily and cumulative variables of prednisolone-equivalent dose (cumulated from 1 year before the start of follow-up) and estimated incidence rates and adjusted hazard ratios (HRs) for hypertension using Cox regression analysis. RESULTS: Among 71 642 patients in the cohort, 24 896 (34.8%) developed hypertension during a median follow-up of 6.6 years. The incidence rate of hypertension was 46.7 (95% confidence interval [CI] 46.0-47.3) per 1000 person-years. Incidence rates increased with higher cumulative glucocorticoid prednisolone-equivalent dose, from 44.4 per 1000 person-years in periods of nonuse to 45.3 per 1000 person-years for periods with between > 0.0 and 959.9 mg (HR 1.14, 95% CI 1.09-1.19), to 49.3 per 1000 person-years for periods with 960-3054.9 mg (HR 1.20, 95% CI 1.14-1.27), and to 55.6 per 1000 person-years for periods with ≥ 3055 mg (HR 1.30, 95% CI 1.25-1.35). Cumulative effects were seen for the 6 diseases studied, but dose-response effects were not found for daily dose. INTERPRETATION: ClinicalTrials. gov, no. NCT03760562.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".