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Record W2763098236 · doi:10.2807/esw.07.08.02168-en

Targeted mass treatment for syphilis with oral azithromycin in Vancouver, Canada

2003· article· en· W2763098236 on OpenAlexaboutno aff
Catherine M Lowndes, Ian Simms

Bibliographic record

VenueWeekly releases (1997–2007) · 2003
Typearticle
Languageen
FieldMedicine
TopicSyphilis Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsSyphilisOutbreakMedicineDemographyDowntownPopulationMen who have sex with menDisadvantagedIncidence (geometry)Sex workHuman immunodeficiency virus (HIV)GerontologyEnvironmental healthVirology

Abstract

fetched live from OpenAlex

A recent paper by Rekart and colleagues (1) presented the findings of a mass treatment intervention to eliminate an outbreak of syphilis in Vancouver, British Columbia. Following several years of very low reported infectious syphilis rates in British Columbia (less than 0.5 per 100 000 population (2), with the majority of cases acquired overseas, there was a marked increase in numbers of reported cases from mid-1997 onwards. This was largely due to a geographically localised outbreak in Vancouver’s disadvantaged downtown eastside area, with rates reaching 126 per 100 000 in this area in 1999. Sixty five percent of the 277 cases reported were among persons who had contact with a potential source of their infection in or from this area. The outbreak was spread mainly through heterosexual contact, with 42% of patients associated with the sex industry (18% sex workers and 24% clients, (2)). Only 6% of cases were in men who have sex with men (MSM). Given high HIV rates among among injection drug users (IDUs) in Vancouver, and the frequent involvement of female IDUs involved in sex work, rising syphilis incidence could facilitate the spread of HIV infection both within the disadvantaged population of Vancouver’s downtown eastside itself, as well as outwards from sex workers through clients into the general population (2).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.019
GPT teacher head0.254
Teacher spread0.235 · 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.

Study designNot applicable
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

Citations0
Published2003
Admission routes1
Has abstractyes

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