The Association between Herpes Zoster and Increased Cancer Risk: A Nationwide Population-Based Matched Control Study
Bibliographic record
Abstract
Background: Herpes zoster (HZ) is strongly associated with decreased immune function, a factor of cancer development. Previous studies suggested inconsistent results regarding the association between HZ and increased cancer risk. We aimed to analyze the association between HZ and specific cancer risk. Methods: Of 134,454 patients diagnosed with HZ between 2002 and 2015, 81,993 HZ patients were matched 1:1 with non-HZ individuals by age, sex, and Charlson comorbidity index. Both groups were examined at 1, 3, and 5 years for cancer diagnosis. A Cox proportional hazard regression model was used to estimate cancer risk in both groups. The postherpetic neuralgia (PHN) and non-HZ groups were compared for specific cancer risk. Results: The HZ group showed a slightly decreased overall cancer risk compared with the non-HZ group (hazard ratio [HR] 0.94, 95% confidence interval [CI] 0.90–0.97, p = 0.002). The HRs for specific cancer risk were 0.41 (95% CI, 0.33–0.50, p < 0.001); 0.86 (95% CI, 0.81–0.91, p < 0.001); 0.87 (95% CI, 0.78–0.97, p = 0.014); 0.80 (95% CI 0.73–0.87, p < 0.001); 1.20 (95% CI, 1.07–1.34, p = 0.001); and 1.66 (95% CI, 1.35–2.03, p < 0.001) for cancers of the lips, mouth, and pharynx; digestive system; respiratory system; unknown secondary and unspecified sites; thyroid and endocrine glands; and lymphoid and hematopoietic systems, respectively. The HZ with PHN group showed higher HR for specific cancer risk, such as lymphoid and hematopoietic systems (95% CI, 1.27–2.39, p < 0.001). Conclusion: HZ was associated with increased or decreased incidence of specific cancers. PHN further increased the risk of developing certain cancers in HZ 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".