Tramadol versus codeine and the short‐term risk of cardiovascular events in patients with non‐cancer pain: A population‐based cohort study
Bibliographic record
Abstract
AIMS: The effect of tramadol on the cardiovascular system is largely unknown. There is concern that, with its multimodal mechanism of action to increase serotonin and norepinephrine levels in the body, it could increase the risk of arterial ischaemia and cardiovascular events. We aimed to compare the short-term risk of cardiovascular events with the use of tramadol to that of codeine among patients with non-cancer pain. METHODS: We conducted a retrospective population-based cohort study using data from the Clinical Practice Research Datalink (CPRD) with new users of tramadol or codeine from April 1998 to March 2017. Exposure was defined using an approach analogous to an intention-to-treat, with a maximum follow-up of 30 days. The primary endpoint was myocardial infarction, and secondary endpoints were unstable angina, ischaemic stroke, coronary revascularization, cardiovascular death and all-cause mortality. Hazard ratios (HRs) were estimated using Cox proportional hazards models, adjusted for high-dimensional propensity score. RESULTS: The final cohort included 123 394 tramadol users and 914 333 codeine users. When tramadol was compared to codeine, the adjusted hazard ratio (HR) of myocardial infarction was 1.00 (95% CI 0.81-1.24). There was also no evidence of elevated risks of unstable angina (0.92; 95% CI 0.67-1.27), ischaemic stroke (0.98; 95% CI 0.82-1.17), coronary revascularization (0.97; 95% CI 0.69-1.38), cardiovascular death (1.07; 95% CI 0.93-1.23) or all-cause mortality (1.03; 95% CI 0.94-1.14) when tramadol was compared to codeine. CONCLUSIONS: Short-term use of tramadol, compared with codeine, was not associated with an increased risk of cardiac events among patients with non-cancer pain.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".