Evaluating the epidemiology of vaginitis in a contemporary cohort: a population-based study
Bibliographic record
Abstract
Introduction: (TV) commonly cause infectious vaginitis in women, especially those of reproductive age. Characterization of the epidemiology of infectious vaginitis in a contemporary population-based cohort was done to determine the longitudinal age-standardized and sex-based testing, positivity, and incidence rates and ratios of infectious vaginitis in a large Canadian healthcare region. Methods: We conducted a population-based cohort study from March 2015 through March 2018 using the Alberta Precision Laboratories (APL) microbiology database. Calgary 2016 census data was used to calculate incidence rates (IR) and ratios (IRR) for cases and testing rates. Results: For testing and positivity, female sex, and younger age groups were associated with increased risk of BV, VVC, and TV infections. The annual mean population in 2016 was 1,411,660 individuals (50.3% female). A total of 40,259 cases/293,853 tests (13.7%) of BV, 32,894 cases/293,853 tests (11.2%) of VVC, and 2018 cases/342,986 tests (0.7%) of TV were identified. The overall IR for BV ranged from 0 to 609 cases per 10,000 person-years. The overall IR for VVC ranged from 0 to 445 per 10,000 person-years. The overall IR for TV ranged from 0 to 27 per 10,000 person-years. The highest age-specific testing positivity rate and IR rate for BV and VVC occurred in women aged 20-34 years and 20-34 years. For TV, female IR for BV, VVC, and TV remained stable during the study. Discussion: These unique regional data provide insight for the development of appropriate age-specific clinical testing criteria according to relative risk of acquisition of each vaginitis agent.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".