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Record W3203937897 · doi:10.15326/jcopdf.2021.0222

Increased Health Care Resource Utilization and Costs Associated with Herpes Zoster Among Patients Aged ≥50 Years with Chronic Obstructive Pulmonary Disease in the United States

2021· article· en· W3203937897 on OpenAlexaff
Parinaz Ghaswalla, Philippe Thompson‐Leduc, Wendy Y. Cheng, Colin Kunzweiler, Min‐Jung Wang, Michael Bogart, Brandon J. Patterson, Mei Sheng Duh, John Wojciehowski, Suna Park, Barbara P. Yawn

Bibliographic record

VenueChronic Obstructive Pulmonary Diseases Journal of the COPD Foundation · 2021
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsPulmonary diseaseMedicineDiseaseHealth careChronic diseaseIntensive care medicinePediatricsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) are potentially at increased risk of herpes zoster (HZ). Little is known about the impact of an HZ episode on health care resource utilization (HRU) and costs among patients with COPD. METHODS: This retrospective cohort study of individuals aged ≥50 years in the United States (US) used administrative claims data from Optum's de-identified Clinformatics Data Mart Database for commercially insured and Medicare Advantage members (2013-2018). Two cohorts of patients with COPD, with HZ (COPD+/HZ+) and without HZ (COPD+/HZ-), were identified. All-cause and COPD-related HRU rates and costs (2018 US dollars) were compared between cohorts for up to 12 months of follow-up. Comparisons were controlled for baseline differences through propensity score adjustment. RESULTS: <0.004) in the year after HZ. CONCLUSIONS: HRU and cost burden is higher in patients with COPD with vs without HZ. These results could help to estimate the potential cost benefits of HZ vaccination among patients with COPD.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.255
Teacher spread0.245 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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