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Record W4386195207 · doi:10.1111/irv.13186

Infection‐induced seroconversion and seroprevalence of SARS‐CoV‐2 among a cohort of children and youth in Montreal, Canada

2023· article· en· W4386195207 on OpenAlexafffundabout
Kate Zinszer, Katia Charland, Laura Pierce, Adrien Saucier, Marie‐Ève Hamelin, Margot Barbosa Da Torre, Julie Carbonneau, Cat Tuong Nguyen, Gaston De Serres, Jesse Papenburg, Guy Boivin, Caroline Quach

Bibliographic record

VenueInfluenza and Other Respiratory Viruses · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineMcGill University Health CentreMinistère de la Santé et des Services Sociaux (Québec)Montreal Children's HospitalInstitut National de Santé Publique du QuébecUniversité LavalUniversité de Montréal
FundersInstitut National de Santé Publique du QuébecPublic Health AgencyPublic Health Agency of Canada
KeywordsSeroconversionSeroprevalenceCohortMedicineSerologyDemographyProspective cohort studyCoronavirus disease 2019 (COVID-19)Cohort studySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyImmunologyInternal medicineVirusAntibodyDisease

Abstract

fetched live from OpenAlex

The EnCORE study is a prospective serology study of SARS-CoV-2 in a cohort of children from Montreal, Canada. Based on data from our fourth round of data collection (May-October 2022), we estimated SARS-CoV-2 seroprevalence and seroconversion. Using multivariable regression, we identified factors associated with seroconversion. Our results show that previously seronegative children were approximately 9-12 times more likely to seroconvert during the early Omicron-dominant period compared to pre-Omicron rounds. Unlike the pre-Omicron rounds, the adjusted rate of seroconversion among 2- to 4-year-olds was higher than older age groups. As seen previously, higher seroconversion rates were associated with ethnic/racial minority status.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.037
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.097
GPT teacher head0.362
Teacher spread0.265 · 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

Citations8
Published2023
Admission routes3
Has abstractyes

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