A Critical Appraisal of Risk Models for Predicting Sexually Transmitted Infections
Bibliographic record
Abstract
BACKGROUND: Prediction rules have been proposed as alternatives to screening recommendations and have potential applications in sexual health decision making. To our knowledge, there has been no review undertaken providing a critical appraisal of existing prediction rules in sexual health contexts. This review aims to identify and characterize prediction rules developed and validated for sexually transmitted infection (STI) screening, describe the methodological issues essential to the suitability of derived models for clinical or public health application, and synthesize the literature on the performance of these models. METHODS: We searched MEDLINE (2003-2012) to identify studies that reported on models predicting STIs. We explored the methodological quality of the studies based on a 16-item quality assessment checklist. We also evaluated the studies based on data extracted on model discrimination, calibration, sensitivity, and testing efficiency. RESULTS: We identified 16 publications reporting on STI prediction rules. The most poorly addressed quality items were missing values, calibration measures, and variable definition. Overall, the performance of risk models as measured by discrimination (area under the receiver operating characteristic curve range, 0.64-0.88) and calibration was found to be generally good or satisfactory. Eight studies attained or were close to attaining the performance benchmark of testing less than 60% of the target population to achieve 90% sensitivity. The 2 risk models that were externally validated displayed adequate discrimination in new settings. CONCLUSIONS: Although we identified several well-performing STI risk prediction rules, few have been validated. Future developments in the use of prediction rules should address their clinical consequence, comparative usefulness, external validity, and implementation impact.
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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 | 0.465 | 0.771 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.027 | 0.012 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".