Prevalence of Cervical-Vaginal Infections in the Pap-Smear Samples in Iran
Bibliographic record
Abstract
UNLABELLED: Cervical-vaginal infection is one of the most common problems in clinical medicine .We aimed to determine the prevalence of cervical-vaginal infections in pap-smear samples from women in urban and rural areas. METHOD: It was a cross - sectional study which had done on 1448 non-pregnant women those had attended 12 health centers in the Dashte- Azadegan city during 2007-2011, Iran. After explained the aim of study, all subjects had signed informed consent, questionnaires regarding demographic and reproductive characteristics, and contraceptive methods used were completed by researcher. Also, pap-smear samples were prepared by a trained obstetrician and sent it to the pathology laboratory. All data were analyzed using SPSS (version 19). Descriptive and analytical statistics (chi - square test) were also applied. RESULTS: The result showed that 55.9% and 44.1% of subjects were respectively in urban and rural areas. The mean age of women was 28±8.075. Pap smear results had shown that 8.8% of samples were infected with one of microorganisms such as Chlamydia, Candida, Cardnerella, and Trichomonas. A significant association was seen between contraceptive methods, education levels and place of residence with cervical-vaginal infections. CONCLUSION: The most prevalent pathogens by descending order were: Candida, Trichomonas and Gardenerella. The prevalence of cervical-vaginal infections was consistent with the results of many studies but it was different with the results of some studies. This could be due to the special conditions of social, economic and cultural of each area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".