Deriving and Validating A Risk Estimation Tool for Screening Asymptomatic Chlamydia and Gonorrhea
Bibliographic record
Abstract
BACKGROUND: There has been considerable interest in the development of innovative service delivery modules for prioritizing resources in sexual health delivery in response to dwindling fiscal resources and rising infection rates. METHODS: This study aims to derive and validate a risk scoring algorithm to accurately identify asymptomatic patients at increased risk for chlamydia and/or gonorrhea infection. We examined the electronic records of patient visits at sexual health clinics in Vancouver, Canada. We derived risk scores from regression coefficients of multivariable logistic regression model using visits between 2000 and 2006. We evaluated the model's discrimination, calibration, and screening performance. Temporal validation was assessed in visits from 2007 to 2012. RESULTS: The prevalence of infection was 1.8% (n = 10,437) and 2.1% (n = 14,956) in the derivation and validation data sets, respectively. The final model included younger age, nonwhite ethnicity, multiple sexual partners, and previous infection and showed reasonable performance in the derivation (area under the receiver operating characteristic curve = 0.74; Hosmer-Lemeshow P = 0.91) and validation (area under the receiver operating characteristic curve = 0.64; Hosmer-Lemeshow P = 0.36) data sets. A risk score cutoff point of at least 6 detected 91% and 83% of cases by screening 68% and 68% of the derivation and validation populations, respectively. CONCLUSIONS: These findings support the use of the algorithm for individualized risk assessment and have important implications for reducing unnecessary screening and saving costs. Specifically, we anticipate that the algorithm has potential uses in alternative settings such as Internet-based testing contexts by facilitating personalized test recommendations, stimulating health care-seeking behavior, and aiding risk communication by increasing sexually transmitted infection risk perception through the creation of tailored risk messages to different groups.
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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 | 0.030 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".