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Record W3084089454 · doi:10.1101/2020.08.31.20183533

Prevalence of SARS-CoV-2 Infections in a Pediatric Orthopedic Hospital

2020· preprint· en· W3084089454 on OpenAlexaffabout
Ghalib Bardai, Jean Ouellet, Thomas Engelhardt, Gianluca Bertolizio, Zenghui Wu, Frank Rauch

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsShriners Hospitals for Children - Canada
Fundersnot available
KeywordsMedicineAsymptomaticSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicCoronavirus disease 2019 (COVID-19)CoronavirusPediatricsTest (biology)Emergency medicineDiseaseInternal medicineInfectious disease (medical specialty)Biology

Abstract

fetched live from OpenAlex

Abstract This project assessed the prevalence of active and past infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a specialized pediatric institution that did not provide care for coronavirus disease 2019 (Covid-19). The study was performed in Montreal, the city with the highest number of Covid-19 cases in Canada during the early phase of the pandemic. Testing for SARS-CoV-2 RNA in 199 individuals (39 children, 61 accompanying persons, 99 hospital employees) did not reveal active infection in any of the study participants. However, 22 (11%) of study participants had SARSCoV-2 IgG antibodies, indicating prior infection. Ten of these participants did not report symptoms compatible with Covid-19 in the 6 months prior to the study. Thus, although no evidence for active infection was found within the institution, consideration should be given to regular staff testing to detect asymptomatic spreading of SARS-CoV-2. In addition, it could be useful to test accompanying persons in children presenting for surgical procedures.

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 score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.357
Teacher spread0.303 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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