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Record W3174543614 · doi:10.1007/s42770-021-00533-z

Diagnosis of Chlamydia trachomatis genital infections in the era of genomic medicine

2021· review· en· W3174543614 on OpenAlexaff
Seema Shetty, Christina Kouskouti, Uwe Schoen, Nikolaos Evangelatos, Kapaettu Satyamoorthy, Franz Kainer, Angela Brand

Bibliographic record

VenueBrazilian Journal of Microbiology · 2021
Typereview
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsChlamydia trachomatisOmicsMedicineDiseaseIntensive care medicineMedical physicsBioinformaticsPathologyGynecologyBiology

Abstract

fetched live from OpenAlex

PURPOSE: Chlamydial genital infections constitute significant sexually transmitted infections worldwide. The often asymptomatic status of C. trachomatis (CT) infections leads to an increased burden on human reproductive health, especially in middle- and low-income settings. Early detection and management of these infections could play a decisive role in controlling this public health burden. The objective of this review is to provide an insight into the evolution of diagnostic methods for CT infections through the development of new molecular technologies, emphasizing on -omics' technologies and their significance as diagnostic tools both for effective patient management and control of disease transmission. METHODS: Narrative review of the diagnostic methodologies of CT infections and the impact of the introduction of -omics' technologies on their diagnosis by review of the literature. RESULTS: Various methodologies are discussed with respect to working principles, required specifications, advantages, and disadvantages. Implementing the most accurate methods in diagnosis is highlighted as the cornerstone in managing CT infections. CONCLUSION: Diagnostics based on -omics' technologies are considered to be the most pertinent modalities in CT testing when compared to other available methods. There is a need to modify these effective and accurate diagnostic tools in order to render them more available and feasible in all settings, especially aiming on turning them to rapid point-of-care tests for effective patient management and disease control.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.344
Teacher spread0.308 · 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
GenreReview

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

Citations29
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBrazilian Journal of MicrobiologySame topicReproductive tract infections researchFrench-language works237,207