Scoring System Avoids Chlamydia trachomatis Overscreening in Women Seeking Surgical Abortions
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a predictive score to avoid unnecessary screening and prophylactic antibiotic use in abortion clinics by identifying a group of women who are at very low risk for Chlamydia trachomatis (CT) infection. METHODS: This population-based retrospective study includes 1000 women who underwent surgical abortion between January and September 2010. The main outcome measure was the rate of CT infection among women seeking an induced abortion according to sociodemographic and clinical data. The score was developed by using two-thirds of the data set as the derivation sample to identify the strongest predictors of CT. A receiver operating characteristic curve established cutoffs and applied the score to the remaining one-third (validation sample). RESULTS: The rate of CT infection was 6.7%. Three criteria were independently associated with CT: gestation more than 10 weeks (adjusted odds ratio [aOR], 1.96; 95% confidence interval [95% CI], 1.06-3.64), not using contraception (aOR, 2.70; 95% CI, 1.41-5.16), and having 0 or 1 child (aOR, 3.46; 95% CI, 1.34-8.93). The CT score was based on these 3 criteria. The low-risk group was derived from values of the score (probability of CT, 1.3% [95% CI, 0-3.0]). Application of these criteria to the validation data set confirmed the diagnostic accuracy of the score (probability of CT, 0%). Sensitivity was 100% and specificity was 26.9% for the score in the validation data set. When applied to the validation data set, the score avoided 25.4% of CT tests and screened 100% of CT-infected women before surgical abortion. CONCLUSIONS: This easy-to-calculate score may prove useful for avoiding CT test in 25% of patients seeking surgical abortion.
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 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".