Health Care Access and Follow-Up of Chlamydial and Gonococcal Infections Identified in an Emergency Department
Bibliographic record
Abstract
BACKGROUND: We examined 2 potentially important factors influencing successful treatment of Chlamydia trachomatis (Ct) and Neisseria gonorrhoeae (GC) infections identified in an emergency department (ED), health care coverage and reporting the ED as a primary source for health care. METHODS: Adult patients aged 18 to 35 years attending an urban ED were screened for Ct and GC. Patients testing positive were contacted by Disease Intervention Specialists and notified of their infection status. Analyses focus on infected patients for whom we have treatment and follow-up information. We used generalized linear models with log link and binomial error distribution to estimate risk ratios (RRs) and 95% confidence intervals (CI). RESULTS: Of 5537 patients screened in the ED, 348 (6.3%) tested positive for Ct, 143 (2.6%) tested positive for GC, and 43 (0.8%) tested positive for both. Overall, 20% of infected patients did not receive treatment. Among infected patients with no health care coverage 25% (n = 56) were untreated compared with 15% (n = 47) of patients reporting health care coverage (RR: 1.7, 95% CI: 1.2-2.3). Among patients reporting the ED as a primary source for health care 26% (n = 27) were untreated compared with the 18% (n = 77) reporting receiving health care from non-ED sources (RR: 1.4, 95% CI: 1.0-2.1). CONCLUSIONS: EDs often serve as primary care sites for difficult-to-reach populations. We were able to successfully locate and treat the greater part of ED-identified infections. However, one-fifth of infected patients did not receive treatment. ED-based screening programs can benefit from integration with local public health infrastructure to improve notification and treatment services.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".