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Record W4406234863 · doi:10.1002/alz.088064

Recombinant zoster vaccine and reduced risk of dementia: matched‐cohort study using large‐scale electronic health records and machine learning methodology

2024· article· en· W4406234863 on OpenAlexaff
Patrick Schwab, Robyn Widenmaier, Halima Tahrat, Maria Littmann, Bruno Anspach, Carolyn Buser‐Doepner, Andreas Georgiou, Max Horn, Sanjay Kumar, Vitaly Polisky, Aleksei Triastcyn, Cornelia M. van Duijn, Pascal Geldsetzer

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsGlaxoSmithKline (Canada)
Fundersnot available
KeywordsDementiaHealth recordsCohortScale (ratio)MedicineCohort studyElectronic health recordInternal medicineGeographyHealth careDiseaseEconomicsCartography

Abstract

fetched live from OpenAlex

Abstract Background With the world population aging, the number of individuals living with dementia is expected to increase significantly. Vaccination against herpes zoster (HZ) with the live‐attenuated zoster vaccine (ZVL) was associated with a lower risk of being diagnosed with dementia in previous studies. We aimed to determine whether the recombinant zoster vaccine (RZV) immunization is also associated with a reduced risk of dementia diagnosis. Methods This retrospective, observational, matched‐cohort study of adults aged ≥50 years used data from the United States Optum® de‐identified Electronic Health Record data set, covering the period 01‐Oct‐2007–30‐Sep‐2023. A comparison cohort comprising adults vaccinated with the pneumococcal polysaccharide vaccine (PPSV23) was created to mitigate selection bias. Matched cohorts were then formed to compare those vaccinated with ZVL vs PPSV23, RZV vs PPSV23, and RZV vs ZVL, with neither comparison arm having been exposed to the comparator vaccine. Matching (1:1) used a propensity score generated with non‐linear estimators based on 394 variables indicating past diagnoses, medication use, preventive health service uptake, and healthcare service utilization. Using the Nelson‐Aalen estimator, the relative risk (RR) for dementia diagnosis (using ICD‐9/‐10 codes) was compared between the three matched cohorts, at 3 and 5 years post‐vaccination. Results Post‐matching characteristics were balanced between cohorts ( Table ). Compared to PPSV23, ZVL significantly reduced 3‐year (RR: 0.86, 95% confidence interval [CI]: 0.86‐0.90; p<0.0001 ) and 5‐year (RR: 0. 92, 95%CI: 0.89‐0.95; p<0.0001 ) dementia risk. RZV significantly reduced 3‐year (RR: 0.76, 95%CI: 0.69‐0.84; p<0.0001 ) and 5‐year (RR: 0.80, 95%CI: 0.71‐0.90; p<0.0005 ) dementia risk, when compared to PPSV23. Compared to ZVL, RZV was also associated with a significant reduction of 3‐year (RR: 0.73, 95%CI: 0. 60‐0.89; p<0.005 ) and 5‐year (RR: 0.77, 95%CI: 0.64‐0.92; p<0.005 ) dementia risk. Conclusion HZ immunization was associated with a reduced risk of dementia at 3 and 5 years post‐vaccination compared to PPSV23 immunization. RZV was associated with a reduced risk of dementia compared to ZVL at 3 and 5 years post‐vaccination.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.048
GPT teacher head0.350
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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