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Record W2058674946 · doi:10.1080/00365540902919384

Determining SARS sub-clinical infection: A longitudinal seroepidemiological study in recovered SARS patients and controls after an outbreak in a general hospital

2009· article· en· W2058674946 on OpenAlexaff
Zhen Yang, Shixin Wang, Qian Li, Yuming Li, Maoti Wei, Hongsheng Gao, Catherine Donovan, Peter Wang

Bibliographic record

VenueScandinavian Journal of Infectious Diseases · 2009
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMemorial University of Newfoundland
FundersNational Natural Science Foundation of China
KeywordsSerologyMedicineOutbreakAntibodyPopulationImmunologyCohortImmunoglobulin MViral diseaseVirusVirologyImmunoglobulin GInternal medicine

Abstract

fetched live from OpenAlex

A cohort of 67 confirmed SARS patients were prospectively followed for 16 months and were compared with a control population. Serum samples taken at various times were tested for IgG and IgM; dynamic serological changes in these antibodies were described. The positive responses of IgM and IgG antibodies in sera against SARS virus from the first week to the sixth week after onset of the illness in patients with SARS were measured. The ELISA test of IgG antibody was negative in 200 community controls. The positive rate in the SARS high-risk population was 0.61% tested by ELISA and 0.21% by IFA. The high-risk population in this study was defined as those who provided health care and other services to SARS patients during the outbreak. IgG antibody in convalescent serum of patients with SARS revealed an increasing trend, peaking at the 22nd week after onset of illness followed by a slow decline. IgM appeared earlier than IgG and can be better used for early detection. IgG remained at a high level for a much longer period, serving as a good indicator for follow-up and for assessing past exposure. Our results also suggest that sub-clinical infection, if it exists, is very rare.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.015
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.040
GPT teacher head0.379
Teacher spread0.339 · 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

Citations16
Published2009
Admission routes1
Has abstractyes

Explore more

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