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Record W4415184547 · doi:10.1080/14787210.2025.2575044

The second-order effects that the COVID-19 pandemic has had on pediatric populations

2025· review· en· W4415184547 on OpenAlexaff
Lael M. Yonker, David Dredge, Alasdair Munro, Costanza Di Chiara, Nicola Cotugno, Danilo Buonsenso

Bibliographic record

VenueExpert Review of Anti-infective Therapy · 2025
Typereview
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsChildren's & Women's Health Centre of British Columbia
FundersNational Heart, Lung, and Blood Institute
KeywordsPandemicMultidisciplinary approachPsychological interventionPublic healthCoronavirus disease 2019 (COVID-19)Public health interventionsPopulation

Abstract

fetched live from OpenAlex

INTRODUCTION: SARS-CoV-2 can have long-term health consequences that persist beyond acute infection. While this is evident in adults and the elderly, the impact on children and adolescents remains under recognized. Here we navigate the second-order post-acute effects that the COVID-19 has had on the pediatric populations, with the exception of the mental health implication of social restrictions. AREAS COVERED: We outline common scenarios related with SARS-CoV-2 infection encountered in pediatric clinical practice, such as in the Multisystem inflammatory syndrome (MIS-C), Long Covid, neurological and autoimmune complications of Covid-19, immunological impact of the viral infection, as well as epidemiological and public health consequences associated with the implementation of non-pharmacological interventions. EXPERT OPINION: SARS-CoV-2 has had several second-order effects on child health, from a biological, epidemiological, and public health perspective, highlighting the complexity of dealing with new infections and the urgent need to implement multidisciplinary interventions that support the health of people at single person and societal level. Funding on modern surveillance system, preventing strategies and research to better understand and cure post-acute complications of viral infections should be a priority of every funding agency.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.085
GPT teacher head0.434
Teacher spread0.349 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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