Human papillomavirus (HPV) genotype distribution in Malaysia: A systematic review
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV) is a key etiological factor in cervical cancer in both Malaysia and globally. It continues to pose a significant public health challenge. This systematic review aims to delineate the distribution of HPV genotypes across different demographics in Malaysia to inform targeted prevention strategies. METHODS: We conducted a systematic review following PRISMA guidelines, analyzing observational studies published from 2000 onward that reported HPV genotypes in cervicovaginal samples from Malaysian women. The review utilized PubMed, SCOPUS, The Cochrane Library, APA PsycNet, and Google Scholar for literature searches, focusing on studies that employed molecular methods for HPV genotyping. Two reviewers independently screened the articles, extracted data, and assessed study quality using the Newcastle-Ottawa Scale (NOS). A descriptive analysis was performed, and findings were synthesized by genotype, region, and ethnicity. RESULTS: The review included 22 studies from an initial pool of 2,547 articles, encompassing 44,251 women. These studies reported a HPV prevalence of up to 100% in confirmed cervical cancer cases and in general screenings from 4.5 to 47.7%. A total of 28 different HPV genotypes (high- and low-risk) were identified, with HPV16, HPV18, HPV58, HPV52, and HPV33 being the most prevalent high-risk genotypes. Genotype distributions showed significant variation across different states and ethnic groups within Malaysia, highlighting the diverse nature of HPV-related risks. CONCLUSIONS: This review provides a detailed snapshot of the HPV genotype distribution in Malaysia, underscoring the necessity for tailored public health interventions that address the regional and ethnic diversity in HPV prevalence. The findings support the need for targeted vaccination programs and enhanced screening measures to effectively combat the high rates of HPV-related (99%) cervical cancer in Malaysia.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".