Benefit of adjunct universal rectal screening for Chlamydia genital infections in women attending Canadian sexually transmitted infection clinics
Bibliographic record
Abstract
Adding universal rectal screening to urogenital screening should positively impact rectal Chlamydia trachomatis (CT) incidence in affected populations. A dynamic Markov model was used to evaluate costs and outcomes of three rectal CT screening strategies among women attending sexually transmitted infection clinics in Alberta, Canada: universal urogenital-only screening (UG-only), additional selected (exposure-based) rectal screening (UG+SR), and additional universal rectal screening (UG+UR). The model included two mutually exclusive health states: infected and susceptible. Additionally, the model included two rounds of transmission: male sex partners of women infected with rectal-only CT and female sex partners of those men. CT complications impacting patients' quality of life (QALY) were considered. Alberta and Canadian data were used to estimate model inputs. We used a health care perspective, a time period of 10 years, and a discount rate of 3% for analyses. Compared to UG-only screening, the incremental cost effectiveness ratios (ICERs) were CA$34,000 and CA$49,000 per QALY gained for UG+SR and UG+UR screening strategies, respectively. Compared to UG+SR, the ICER was CA$62,000 per QALY gained for the UG+UR strategy. Both adjunct selected and universal rectal screening strategies are cost effective compared to UG-only screening, and UG+UR screening is cost effective when compared to UG+SR screening.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".