A decade data of HPV genotypes in metropolitan regions of Indonesia: paving the way for a national cervical cancer elimination strategy
Bibliographic record
Abstract
OBJECTIVE: Human papillomavirus (HPV) infection is a global public health concern and associated with cervical cancer. HPV genotype mapping has an essential role in prevention and control strategy in developing more suitable HPV vaccine for Indonesia. METHODS: This was a descriptive retrospective cross-sectional study from 2012 until 2022 at Kalgen Laboratory, Jakarta from all over the metropolitan regions. The total 76,413 samples were collected with consecutive sampling, which 694 excluded, thus final samples used were 75,719. HPV DNA test was performed using the polymerase chain reaction (PCR): SPF10-DEIA-LiPA25 methods. HPV genotyping procedures included DNA extraction, PCR using the HPV XpressMatrix kit, and hybridization. RESULTS: From 75,719 samples, 93.4% was negative for intraepithelial lesion or malignancy (NILM). Among 6.6% of total 75,719 samples of abnormal cytology groups, 53.8% were atypical squamous cells of undetermined significance (ASCUS), 32.9% were low grade intraepithelial lesion (LSIL), and 13.3% were high grade intraepithelial lesion (HSIL). The most common high risk HPV genotypes among HSIL were 16, 18, 52, 58, 33, 51, and 53. Single HPV infection was more common compared to multiple infections. CONCLUSION: This study showed that HR-HPV types among HSIL were 16, 18, 52, 58, 33, 51, and 53. HPV 52 was the most frequent type among NILM, ASCUS, and LSIL. Thus, it could serve as a potential future reference to create a more suitable HPV nonavalent vaccine for Indonesian population based on its different epidemiology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".