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Record W2735523205 · doi:10.1136/sextrans-2017-053201

Can enhanced screening of men with a history of prior syphilis infection stem the epidemic in men who have sex with men? A mathematical modelling study

2017· article· en· W2735523205 on OpenAlexafffundabout
Ashleigh R. Tuite, Souradet Y. Shaw, Joss Reimer, Craig Ross, David N. Fisman, Sharmistha Mishra

Bibliographic record

VenueSexually Transmitted Infections · 2017
Typearticle
Languageen
FieldMedicine
TopicSyphilis Diagnosis and Treatment
Canadian institutionsPublic Health OntarioUniversity of TorontoUniversity of ManitobaWinnipeg Regional Health Authority
FundersCanadian Institutes of Health Research
KeywordsSyphilisMedicineMen who have sex with menTransmission (telecommunications)Incidence (geometry)OutbreakDemographyImmunologyVirologyHuman immunodeficiency virus (HIV)Telecommunications

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study is to determine the transmission impact of using prior syphilis infection to guide a focused syphilis screening intervention among men who have sex with men (MSM). METHODS: We parameterised a deterministic model of syphilis transmission in MSM to reflect the 2011-2015 syphilis outbreak in Winnipeg, Canada. Enhanced screening of 75% of men with prior syphilis every 3 months (A) was compared with distributing equivalent number tests to all MSM (B) or those with the highest partner number (C). We compared early syphilis incidence, diagnoses and prevalence after 10 years, relative to a base case of 30% of MSM screened annually. RESULTS: Strategy A was expected to avert 52% of incident infections, 44% of diagnosed cases and reduce early syphilis prevalence by 89%. Strategy B had the least impact. Strategy C was most effective, averting 59% of incident cases. When screening frequency was semiannual or annual, strategy A was the most effective. CONCLUSIONS: Enhanced screening of MSM with prior syphilis may efficiently reduce transmission, especially when identification of high-risk men via self-reported partner numbers or high-frequency screening is difficult to achieve.

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)0.000
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 score0.137
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.037
GPT teacher head0.287
Teacher spread0.250 · 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

Citations23
Published2017
Admission routes3
Has abstractyes

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