Presumptive Treatment of Chlamydia and Gonorrhea Infections in a Canadian Ambulatory Emergency Department Setting
Bibliographic record
Abstract
Objectives Presumptive antibiotic treatment may be given for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) infections before a laboratory diagnosis is established, but overtreatment can increase resistance rates. We sought to determine the presumptive treatment prevalence in our emergency department (ED) setting, as well as the number of overtreated and undertreated patients. Methods We performed a retrospective cohort study of all patients tested for CT/GC in an urban academic ED during a 6-month period in 2015. Presumptive treatment prevalence, overtreatment and undertreatment proportions, and CT- and GC-positive test proportions were calculated with 95% Wald confidence interval (CI) and compared across age and sex. Results Of 209 included cases (male n = 3, female n = 206), 27 (13%; CI, 8%–18%) received presumptive treatment for CT and 19 (9%; CI, 5%–14%) for GC. Seven cases (3%; CI, 1%–6%) were positive for CT and 0 for GC. Of the 7 CT-positive cases, 2 (29%) received presumptive treatment in the ED, and 5 (71%) were treated after the positive test results were obtained. There was no loss to follow-up. Mean delay to treatment was 10 days, including a mean of 3 days for laboratory analysis. Overtreatment and undertreatment proportions were 93% (CI, 83%–100%) and 3% (CI, 0%–5%) for CT and 100% and 0% for GC, respectively. Positive test result, presumptive treatment, overtreatment, and undertreatment were not associated with age or sex. Conclusions Given the low CT/GC incidence and good follow-up, at our institution, it would be reasonable to wait for a laboratory diagnosis rather than give presumptive treatment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".