Prevalence of high-risk human papillomavirus infection and genotype distribution among Kazakhstani women with abnormal cervical cytology
Bibliographic record
Abstract
Aim This study aimed to identify the prevalence and distribution of high-risk human papillomavirus (HR-HPV) types among Kazakhstani women with abnormal cervical cytology.Methods A cross-sectional study was performed from May 2019 to June 2020. Cervical samples were collected from women in the different regions of Kazakhstan.Results A total of 316 patients’ samples were analysed for HR-HPV using real-time multiplex PCR. Cervical cytology abnormalities were reported according to the Bethesda classification. HPV detection by cytology showed a statistically significant association with HPV status and the number of HPV infection types (p < .05). Among women with abnormal cervical cytology, 62.4% were positive for HPV infection of those 79.4% had low-grade squamous intraepithelial lesions (LSIL), and 20.6% had high-grade squamous intraepithelial lesions (HSIL). Among patients with LSIL, 77.4% had HPV16 and 58.8% were infected with HPV18. Among patients with HSIL, 41.2% had HPV18 and 22.6% – HPV16.Conclusions There is a high prevalence of HR-HPV types among Kazakhstani women with abnormal cervical cytology. The most identified types were HPV16, 18, 31, 33 and 52. There is an emergency need to implement an HPV vaccination program to prevent cervical lesion development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".