Incidental Statin Use and the Risk of Stroke or Transient Ischemic Attack after Radiotherapy for Head and Neck Cancer
Bibliographic record
Abstract
Background and PurposeInterventions to reduce the risk for cerebrovascular events (CVE; stroke and transient ischemic attack [TIA]) after radiotherapy (RT) for head and neck cancer (HNCA) are needed.Among broad populations, statins reduce CVEs; however, whether statins reduce CVEs after RT for HNCA is unclear.Therefore, we aimed to test whether incidental statin use at the time of RT is associated with a lower rate of CVEs after RT for HNCA.Methods From an institutional database we identified all consecutive subjects treated with neck RT from 2002 to 2012 for HNCA.Data collection and event adjudication was performed by blinded teams.The primary outcome was a composite of ischemic stroke and TIA.The secondary outcome was ischemic stroke.The association between statin use and events was determined using Cox proportional hazard models after adjustment for traditional and RT-specific risk factors. ResultsThe final cohort consisted of 1,011 patients (59±13 years, 30% female, 44% hypertension) with 288 (28%) on statins.Over a median follow-up of 3.4 years (interquartile range, 0.1 to 14) there were 102 CVEs (89 ischemic strokes and 13 TIAs) with 17 in statin users versus 85 in nonstatins users.In a multivariable model containing known predictors of CVE, statins were associated with a reduction in the combination of stroke and TIA (hazard ratio [HR], 0.4; 95% confidence interval [CI], 0.2 to 0.8; P=0.01) and ischemic stroke alone (HR, 0.4; 95% CI, 0.2 to 0.8; P=0.01).Conclusions Incidental statin use at the time of RT for HNCA is associated with a lower risk of stroke or TIA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".