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P3.217 International Comparison of Recent Trends in the Rates of HIV Diagnoses Among Men Who Have Sex with Men (MSM)

2013· article· en· W2003348016 on OpenAlexaboutno aff
Tim Foster, N Dickson, Peter Saxton

Bibliographic record

VenueSexually Transmitted Infections · 2013
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyMedicineHuman immunodeficiency virus (HIV)PopulationGeographyEnvironmental healthFamily medicine

Abstract

fetched live from OpenAlex

Background After a rise in the early 2000s in the number of new HIV diagnoses among MSM in New Zealand, also witnessed in many developed countries, in 2011 the number dropped by 34% compared to 2010. To assess relative progress on control we compare trends in HIV diagnosis rates among MSM in developed countries with similarly mature epidemics. Methods We obtained data on annual HIV diagnoses among MSM between 2003–2011 from 17 developed countries (Australia, Belgium, Canada, Denmark, Finland, France, Iceland, Ireland, Netherlands, New Zealand, Norway, Spain, Sweden, Switzerland, Germany, UK, US). We reallocated those with unknown means of infection according to the countries’ pattern of known causes, and used countries’ adjustment for delayed reporting where available. The diagnosis rate was derived using the population of men aged 15–64. Results New Zealand has low rates compared to most countries of Western Europe, North America and Australia, and are comparable with those of Scandinavia All counties except New Zealand, Iceland and Canada, experienced a slight overall rise in diagnosis rates in the period 2003–2011 Over the past four years there has been a: Slight trend upwards in UK, Belgium, France, Australia, Ireland No clear trend in Spain, Canada, Germany, Denmark, Norway Slight trend downwards in New Zealand, the Netherlands, Sweden, Finland, and possibly Iceland Clear trend downwards in Switzerland. Conclusions New Zealand has a low rate of HIV diagnoses, relative to many other developed countries. Our drop in 2011 HIV is encouraging but not unique. Limitations of this study are that the data are of diagnosis not infection rates, are influenced by patterns of testing, immigration and emigration, and dual modes of transmission, and the proportion who are MSM may vary between countries. Factors relating to recent trends should be explored.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.029
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0290.004

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 head0.035
GPT teacher head0.342
Teacher spread0.307 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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