The Use of Telmisartan and the Incidence of Cancer
Bibliographic record
Abstract
BACKGROUND: A meta-analysis reported an 8% increased risk of cancer with the use of angiotensin receptor blockers (ARBs), but subsequent meta-analyses and observational studies did not confirm this risk. However, telmisartan comprised 85% of the data in the original meta-analysis. Thus, the objective of this study was to determine whether the use of telmisartan, compared with other ARBs, is associated with an increased risk of cancer. METHODS: We used the United Kingdom Clinical Practice Research Datalink to assemble a cohort of all patients newly treated with ARBs between 2000 and 2008, and followed until December 2010. Time-dependent cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of cancer associated with telmisartan, compared with other ARBs, adjusted for potential confounders. Secondary analyses assessed the risk with each of the 4 most common cancers (lung, breast, prostate, colorectal). RESULTS: The cohort consisted of 62,109 new ARB users, which included 3,438 telmisartan and 58,671 other ARB users. Compared with other ARBs, telmisartan use was not associated with an increased risk of cancer overall (16.3 vs. 15.0 per 1,000 person-years, respectively; adjusted HR: 0.93, 95% CI: 0.81-1.06) or by cancer site (lung, HR: 0.91, 95% CI: 0.55-1.51; breast, HR: 1.28, 95% CI: 0.90-1.82; prostate, HR: 0.79, 95% CI: 0.53-1.18; colorectal, HR: 1.41, 95% CI 0.95-2.10). CONCLUSIONS: Compared with other ARBs, telmisartan is not associated with an increased risk of cancer. This study provides reassurance as to the short-term safety of telmisartan.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".