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Record W2111895226 · doi:10.5539/gjhs.v7n3p249

Latent Syphilis Among Inpatients in an Urban Area of China

2014· article· en· W2111895226 on OpenAlexvenueno aff
Àiyīng Liú, Wen-Jing Zang, Lingling Yuan, Yong-Li Chai, Shuqi Wang

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldMedicine
TopicSyphilis Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsLatent SyphilisSyphilisMedicineTreponemaChinaLatent class modelDemographyEpidemiologyPediatricsObstetricsInternal medicineImmunologyGeographyHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

We aimed at investigating the epidemiological features of latent syphilis among inpatients in an urban area of China. During the period of Jan 1999 to Dec 2007, 146 inpatients were positive for treponema pallidum particle agglutination (TPPA) assay from 22,454 inpatients who were admitted to the China Meitan General Hospital. The number of latent syphilis increased steadily during this period of time. From the 146 TPPA positive inpatients, 137 inpatients were diagnosed as latent syphilis. The number of male patients with latent syphilis was slightly more than the female, but there was no statistical significance (P>0.01). The number of male patients over 60 years old was 42 (30.66%), which was higher than other age groups (p<0.05). The number of female patients at the age range of 20-29 years was 20 (14.60%), which was higher than other age groups (p<0.05). Our results demonstrated that routine syphilis screening among inpatients proves to be one of the most effective precautionary measures to identify latent syphilis and thus to prevent transmission in urban areas in China.

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 score0.026
Threshold uncertainty score0.051

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.333
Teacher spread0.305 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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