Predictors identifying those at increased risk for STDs: a theory-guided review of empirical literature and clinical guidelines
Bibliographic record
Abstract
SummarySexually transmitted diseases (STDs) are leading causes of substantial morbidity worldwide. Identification of risk factors for estimating STD risk provides opportunities for optimising service delivery in clinical settings, including improving case finding accuracy and increasing cost-efficiency by limiting the testing of low-risk individuals. The current study was undertaken to synthesise the evidence supporting commonly cited chlamydia and gonorrhoea risk factors. The level of empirical support for the following predictors was strong/moderate: age, race/ethnicity, multiple lifetime sexual partners, sex with symptomatic partners and concurrent STD diagnosis. The following predictors had weak evidence: socio-economic status, transactional sex, drug/alcohol use, condom use and history of STD diagnosis. The most frequently listed predictors among nine clinical guidelines were younger age and multiple sexual partners; the least consistently listed predictor was inconsistent condom use. We found reasonably good concordance between risk factors consistently listed in the recommendations and predictors found to have strong empirical support in the literature. There is a need to continue building the evidence base to explicate the mechanisms and pathways of STD acquisition. We recommend periodic reviews of the level of support of predictors included in clinical guidelines to ensure that they are in accordance with empirical evidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".