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Record W4221000129 · doi:10.1097/inf.0000000000003527

SARS-CoV-2 Viral Load Quantification, Clinical Findings and Outcomes in Children Seeking Emergency Department Care: Prospective Cohort Study

2022· article· en· W4221000129 on OpenAlexafffund
Stephen B. Freedman, Lisa Oberding, Kelly Kim, Jianling Xie, Byron M. Berenger, Ruza Goulden, Sarah Weisbeck, Dylan R. Pillai

Bibliographic record

VenueThe Pediatric Infectious Disease Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsDigital polymerase chain reactionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Viral loadMedicineReal-time polymerase chain reactionPolymerase chain reactionEmergency departmentProspective cohort studyCoronavirus disease 2019 (COVID-19)Reverse transcriptaseVirology2019-20 coronavirus outbreakHuman immunodeficiency virus (HIV)Internal medicineGeneBiologyGenetics

Abstract

fetched live from OpenAlex

We compared the perfomance of SARS-CoV-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) to droplet digital PCR (ddPCR). 95% and 40% of positive and negative RT-PCR specimens, respectively, were positive on ddPCR yielding sensitivities of 84% (95% CI: 74, 91) and 97% (95% CI: 89, 99), for RT-PCR and ddPCR, respectively. We found that SARS-CoV-2 RT-PCR testing in children has a concerning false-negative rate at lower nucleocapsid gene copy numbers.

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)0.002
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 score0.003
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.332
Teacher spread0.311 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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