The prevalence of risk factors for gastrointestinal complications and use of gastroprotection among persons hospitalized for cardiovascular disease
Bibliographic record
Abstract
BACKGROUND: Aspirin is often used in patients with cardiovascular disease, but it can also cause gastrointestinal complications. Proton pump inhibitors reduce the risk of gastrointestinal complications in aspirin users with a history of gastrointestinal complications. AIM: To determine the prevalence of gastrointestinal risk factors in aspirin users and the prevalence of proton pump inhibitor utilization in high-risk patients. METHODS: We reviewed all patients admitted to hospital between April and October 2004 with a diagnosis of cardiovascular disease. We collected data on demographics, medication use, comorbid illnesses, previous gastrointestinal complications, and medication use on admission and discharge. RESULTS: A total of 324 patients were admitted with cardiovascular disease of whom 94% were discharged on aspirin. Seventy-eight per cent of patients admitted had at least one gastrointestinal risk factor in addition to having cardiovascular disease, and 15% had three or more additional gastrointestinal risk factors. Patients with additional gastrointestinal risk factors were more likely to be prescribed proton pump inhibitor therapy (27% vs. 10%, P < 0.001). Only 10% of proton pump inhibitor-naíve high-risk aspirin users were prescribed a proton pump inhibitor upon discharge. CONCLUSIONS: The majority of high-risk aspirin users are not receiving proton pump inhibitors for gastroprotection. Further work is required to encourage providers to consider the use of gastroprotective strategies in appropriate patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".