Ischaemic cardiovascular risk and prescription of non-steroidal anti-inflammatory drugs for musculoskeletal complaints
Bibliographic record
Abstract
OBJECTIVE: To determine the influence of ischaemic cardiovascular (CV) risk on prescription of non-steroidal anti-inflammatory drugs (NSAIDs) by general practitioners (GPs) in patients with musculoskeletal complaints. DESIGN: Cohort study. SETTING: A healthcare database containing the electronic GP medical records of over one million patients throughout the Netherlands. PATIENTS: A total of 474 201 adults consulting their GP with a new musculoskeletal complaint between 2000 and 2010. Patients were considered at high CV risk if they had a history of myocardial infarction, angina pectoris, stroke, transient ischaemic attack, or peripheral arterial disease, and at low CV risk if they had no CV risk factors. MAIN OUTCOME MEASURES: Frequency of prescription of non-selective (ns)NSAIDs and selective cyclooxygenase-2 inhibitors (coxibs). RESULTS: Overall, 24.4% of patients were prescribed an nsNSAID and 1.4% a coxib. Of the 41,483 patients with a high CV risk, 19.9% received an nsNSAID and 2.2% a coxib. These patients were more likely to be prescribed a coxib than patients with a low CV risk (OR 1.9, 95% CI 1.8-2.0). Prescription of nsNSAIDs decreased over time in all risk groups and was lower in patients with a high CV risk than in patients with a low CV risk (OR 0.8, 95% CI 0.7-0.8). CONCLUSION: Overall, patients with a high CV risk were less likely to be prescribed an NSAID for musculoskeletal complaints than patients with a low CV risk. Nevertheless, one in five high CV risk patients received an NSAID, indicating that there is still room for improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".