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Record W4383368409 · doi:10.1016/j.eclinm.2023.102089

Why has the epidemiology of RSV changed during the COVID-19 pandemic?

2023· review· en· W4383368409 on OpenAlexafffund
Bahaa Abu-Raya, Marina Viñeta Paramo, Frederic Reicherz, Pascal M. Lavoie

Bibliographic record

VenueEClinicalMedicine · 2023
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersDeutsche ForschungsgemeinschaftBC Children's HospitalMichael Smith Health Research BCChildren's Hospital FoundationGovernment of CanadaBC Children’s Hospital Foundation
KeywordsMedicinePandemicEpidemiologyVirusCoronavirusImmunologyVirologyCoronavirus disease 2019 (COVID-19)Intensive care medicineDiseaseInfectious disease (medical specialty)Internal medicine

Abstract

fetched live from OpenAlex

The coronavirus disease 2019 (COVID-19) pandemic has drastically perturbed the epidemiology of Respiratory Syncytial Virus (RSV) respiratory tract infections in children. The reasons for this are not clear. In this article, we review the current literature and critically discuss the different theories to explain why the epidemiology of RSV has changed during the COVID-19 pandemic. Proposed mechanisms include decreased viral immunity in vulnerable age groups caused by the prolonged lack of RSV circulation early in the pandemic, potential Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2)-induced immune dysregulation, viral interactions between SARS-CoV-2 and RSV, and modifications in health-seeking behaviors as well as heath systems factors. Research in viral genomics and phylogeny, and more robust immunology research is needed to guide RSV prevention and health care resource planning.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.744
GPT teacher head0.603
Teacher spread0.141 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations185
Published2023
Admission routes2
Has abstractyes

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