Risk of incident gout following exposure to recombinant zoster vaccine in US adults aged ≥50 years
Bibliographic record
Abstract
OBJECTIVE: To assess whether recombinant zoster vaccine (RZV) is associated with an increased risk of new-onset gout among US adults aged ≥50 years. METHODS: We conducted a real-world, retrospective safety study with a self-controlled risk interval (SCRI) design using administrative claims data. We included health plan members aged ≥50 years with RZV exposure, followed by incident gout within 60 days. Days 1-30 following RZV exposure were considered the risk window (RW), and days 31-60 were considered the control window (CW). We estimated the risk ratio (RR) of gout in the RW versus CW, using a conditional Poisson model. The primary analysis estimated the risk of incident gout following any RZV dose. Sensitivity analyses evaluated dose 1- and dose 2-specific risks, risk among patients compliant with recommended dose spacing of 60-183 days, adjustment for seasonality, and restriction to the pre-COVID-19 era (before December 1, 2019). RESULTS: A total of 461,323 individuals received ≥1 RZV dose; we included 302 individuals (mean age 72.5 years; 66 % male) with evidence of new-onset gout within 60 days in SCRI analyses. A total of 153 (50.7 %) individuals had gout events in the RW and 149 (49.3 %) in the CW (RR 1.03; 95 % confidence interval 0.81, 1.29). All sensitivity analyses had consistent results, with no association of RZV with incident gout. CONCLUSION: In a population of US adults aged ≥50 years, there was no statistically significant increase in the risk of gout during the 30 days immediately after RZV exposure, compared with a subsequent 30-day CW.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".