STI initiative: Improving testing for sexually transmitted infections in women
Bibliographic record
Abstract
Canadian urgent care and walk-in medical clinics provide health care for a population that may be poorly covered by traditional health care structures. Despite evidence suggesting that women with urinary complaints experience a high incidence of sexually transmitted infections (STIs), this population may be under-tested in this particular setting. The aim of this quality improvement initiative was to increase STI testing in women presenting with GU complaints. Implementation of an opt-out method of STI testing for women ages 16 and older was introduced at three walk-in clinics. Women presenting with GU complaints were given the opportunity to provide samples for both conventional urine culture and nucleic acid amplification testing (NAAT) for non-viral STIs. Patients received treatment according to standard of care and public health was notified as per local regulations. Testing rate and STI incidence was tracked via clinic electronic medical records (EMRs). Overall results were tracked using run charts and compared to historical data for the year prior to the start of the project. Over a 1 year period prior to this intervention, only 65 STI tests were performed in over 1100 GU complaints (5.5%). Six STIs were identified during this time. During the 36-week project period, testing increased to 45% of the patient population (320/707). The STI detected incidence increased from 0.51% to 1.4% in all women, and from 0.84% to 3.4% in women aged 16-29 years. An opt-out method was an effective intervention for increasing STI testing within the walk-in clinic setting. With optimisation, significant increases in testing rates can be obtained without substantially increasing clinic workload and at no economic cost to the clinic. As expected, detected incidence rates of STIs were higher than the recognised population prevalence.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".