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Sexually Transmitted Infections: Old Foe, New Opportunities for Control

2025· reference-entry· en· W4411326575 on OpenAlexaff
Francis Ndowa, Suzanne M. Garland, Remco P. H. Peters, Laith J. Abu-Raddad

Bibliographic record

VenueOxford Research Encyclopedia of Global Public Health · 2025
Typereference-entry
Languageen
FieldMedicine
TopicSyphilis Diagnosis and Treatment
Canadian institutionsSKiN Health
Fundersnot available
KeywordsControl (management)BiologyMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract More than 30 pathogens, including bacteria, viruses, protozoa, and ectoparasites, cause sexually transmitted infections (STIs). Approximately 1 million curable STIs, comprising Neisseria gonorrhoeae, Chlamydia trachomatis, Treponema pallidum, and Trichomonas vaginalis, are acquired everyday worldwide. The most common of the treatable STIs is a protozoon, T. vaginalis, causing approximately 156 million new infections in 2020. Sexually transmitted viral infections are also prevalent worldwide, of which the most important are the human immunodeficiency virus (HIV), herpes simplex virus types 1 and 2, and the human papillomavirus (HPV). STIs impact people’s lives through their impact on reproductive health and child health, as well as through the facilitation of sexual transmission of HIV infection and, with some, such as HPV, as precursors of anogenital cancers. People face enormous challenges with access to health services for STI care. Furthermore, some STIs commonly exist as asymptomatic infections, particularly among adolescents. In addition, even with symptoms, some individuals have difficulty accessing affordable healthcare services because of anticipated stigma. The epidemiology of STIs is influenced by an interplay of the determinants of spread of infections and human behavior. At the individual level, determinant factors include ignorance of STIs, sexual behavior, sexual preferences, sexual networks, sex work, healthcare-seeking behaviors, and whether or not use is made of old and newer biomedical HIV and STI prevention interventions, such as condoms, medical male circumcision, and pre-exposure prophylaxis (e.g., PrEP for HIV); availability of, and access to, post-exposure prophylaxis; and prophylactic STI vaccines. At the population level, the determinants include demographic factors, socioeconomic factors, geographical settings, cultural ramifications, political commitment and health system responses. STIs can be prevented through modification of sexual behavior toward “safer sex.” The interventions implemented by countries, to varying degrees of coverage, include behavioral interventions, promotion of use of barrier methods, vaccinations, screening for STIs, and case-finding in people attending healthcare services for conditions other than for STI care. In persons with established infections, the focus is on averting short-term and long-term sequelae of untreated STIs, such as pelvic inflammatory disease, tubal-factor infertility, adverse pregnancy outcomes, and cervical cancer. This includes screening for asymptomatic STIs, cervical cancer, and the early diagnosis and treatment of HIV infection. Regular screening has been shown to reduce the population prevalence of STIs, prevention of some adverse outcomes such as congenital syphilis, and improved prognosis in such cases as early treatment of HPV-associated cervical cancer. The evidence and cost-effectiveness of screening for C. trachomatis and N. gonorrhoeae to prevent infertility and improve pregnancy outcomes is limited. Also, screening programs result in increased use of antibiotics in certain population groups, with concerns of increase in the development of antimicrobial resistance, especially in N. gonorrhoeae. The detection and management of STIs has been revolutionized since the 1990s by the development of molecular detection tests, the advent of STI vaccines, and the advent of new treatment molecules, such as antiretroviral treatments, which have facilitated timely diagnosis of both symptomatic and asymptomatic infections and converted some fatal infections into manageable chronic infections.

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)0.003
metaresearch head score (Gemma)0.003
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: Other · Consensus signal: none
Teacher disagreement score0.723
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.158
GPT teacher head0.416
Teacher spread0.258 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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