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Record W2120975758 · doi:10.1017/s0950268813001453

Severe hand, foot and mouth disease in Shenzhen, South China: what matters most?

2013· article· en· W2120975758 on OpenAlexaff
Jin Mou, Martin Dawes, Y. LI, Yaqing He, Hongxia Ma, Xiongwei Xie, Siân Griffiths, Jinquan Cheng

Bibliographic record

VenueEpidemiology and Infection · 2013
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineOdds ratioEpidemiologyEtiologyMultivariate analysisHand-foot-and-mouth diseaseDiseaseFoot (prosody)Socioeconomic statusPediatricsInternal medicineOutpatient clinicDemographyEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Case report data and a matched case-control study were used to investigate the epidemiological characteristics of hand, foot and mouth disease (HFMD) in children in Shenzhen, China between 2008 and 2011. Multivariate analyses were used to evaluate factors associated with severity of infection. Laboratory tests were performed to determine aetiological identification for samples from 163 severe and fatal cases as well as an outpatient-based HFMD sentinel surveillance system (n = 446). All identified EV71 belonged to sub-genotype C4a. No major changes in the CA16 and EV71 viruses were found until the end of 2011. Annual attack rates and the case-severity ratios (CSRs) rose from 0.82/1000 and 0.56/1000, respectively, in 2008 to 2.12/1000 and 6.13/1000 in 2011. The CSR was higher in migrants than in local residents. The adjusted odds ratio (OR) of having a severe attack for being a migrant was 2.45, having a fever >39°C (OR 5.77), visiting a private clinic (OR 2.65), longer time from symptom onset to diagnosis (OR 1.49), visiting a doctor (OR 1.51), early use of intramuscular pyrazolone (OR 3.36), early use of intravenous glucocorticoids (OR 2.28), or the combination of both (OR 3.75). The mortality and increasing case severity appears to be associated with socioeconomic factors including migration and is of worldwide concern.

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)0.001
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 score0.020
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.312
Teacher spread0.282 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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