Molecular characterization of high-risk human papillomavirus (HR-HPV) in women in Lomé, Togo
Bibliographic record
Abstract
BACKGROUND: The causative agent of cervical cancer referred to as Human papillomavirus (HPV) remains a real public health problem. Many countries in West Africa, such as Togo have no data on the high-risk HPV (HR-HPV) infection and genotypes distribution. In order to fill the knowledge gap in the field in Togo, the main objective of this study was to determine the prevalence of pre-cancerous lesions of the cervix and HR-HPV genotypes among Togolese women. METHODS: Samples were collected from 240 women by introducing a swab in the cervix. Then, the screening of precancerous cervical lesions using the visual inspection with acetic acid and lugol (VIA / VIL) was conducted. The HR-HPV genotypes were characterised by real-time multiplex PCR. RESULTS: Out of 240 women recruited, 128 (53.3%) were infected by HR-HPV. The most common genotypes were HPV 56 (22.7%), followed by HPV 51 (20.3%), HPV 31 (19.5%), HPV 52 (18.8%) and HPV 35 (17.2%). The least common genotypes were HPV 33 (2.3%) and HPV 16 (2.3%). Among the women, 1.3% (3/240) were positive to VIA/VIL. CONCLUSION: This study allowed HR-HPV genotypes to be characterised for the first time in Lomé, Togo. This will help in mapping the HR-HPV genotypes in West Africa.
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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) | 0.001 | 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".