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Record W3158478043 · doi:10.1093/eurpub/ckab079

SARS-CoV-2 infections in kindergartens and associated households at the start of the second wave in Berlin, Germany—a cross-sectional study

2021· article· en· W3158478043 on OpenAlexaboutno aff
Marlene Thielecke, Stefanie Theuring, Welmoed van Loon, Franziska Hommes, Marcus Mall, Alexander Rosen, Falko Böhringer, Christof von Kalle, Valerie Kirchberger, Tobias Kurth, Joachim Seybold, Frank P. Mockenhaupt, Tanja Chylla, Elisabeth Linzbach, Annkathrin von der Haar, Jennifer Körner, Maximilian Gertler, Julian Bernhard, Heike Rössig, Marco Kurzmann, Frederike Peters, Christoph Wiesmann, Johanna Horn, Julia Steger, Norma Bethke, Tobias Schmidergall, F. Holz, Antje van den Berg, Maria Luz Peña-Groth

Bibliographic record

VenueEuropean Journal of Public Health · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)PandemicCross-sectional studySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Transmission (telecommunications)Medicine2019-20 coronavirus outbreakPediatricsEnvironmental healthDemographyVirologyGeographyDiseaseInfectious disease (medical specialty)SociologyInternal medicineOutbreak

Abstract

fetched live from OpenAlex

Actual surveys in kindergartens on SARS-CoV-2 infections are rare. At the beginning of the second pandemic wave, we screened 12 randomly selected kindergartens in Berlin, Germany. A total of 720 participants (pre-school children, staff and connected household members) were briefly examined and interviewed, and SARS-CoV-2 infections and anti-SARS-Cov-2 IgG antibodies were assessed. About a quarter of the participants showed common cold-resembling symptoms. However, no SARS-CoV-2 infection was detected, and only one childcare worker showed IgG seroreactivity. Against a backdrop of increased pandemic activity in the community, this cross-sectional study does not suggest that kindergartens are silent transmission reservoirs.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.139
GPT teacher head0.380
Teacher spread0.241 · 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 teacher head, 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

Citations14
Published2021
Admission routes1
Has abstractyes

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