Human papillomavirus distribution in invasive cervical carcinoma in sub‐Saharan Africa: could HIV explain the differences?
Bibliographic record
Abstract
OBJECTIVES: To describe human papillomavirus (HPV) distribution in invasive cervical carcinoma (ICC) from Mali and Senegal and to compare type-specific relative contribution among sub-Saharan African (SSA) countries. METHODS: A multicentric study was conducted to collect paraffin-embedded blocks of ICC. Polymerase chain reaction, DNA enzyme immunoassay and line probe assay were performed for HPV detection and genotyping. Data from SSA (Mozambique, Nigeria and Uganda) and 35 other countries were compared. RESULTS: One hundred and sixty-four ICC cases from Mali and Senegal were tested from which 138 were positive (adjusted prevalence = 86.8%; 95% CI = 79.7-91.7%). HPV16 and HPV18 accounted for 57.2% of infections and HPV45 for 16.7%. In SSA countries, HPV16 was less frequent than in the rest of the world (49.4%vs. 62.6%; P < 0.0001) but HPV18 and HPV45 were two times more frequent (19.3%vs. 9.4%; P < 0.0001 and 10.3%vs. 5.6%; P < 0.0001, respectively). There was an ecological correlation between HIV prevalence and the increase of HPV18 and the decrease of HPV45 in ICC in SSA (P = 0.037 for both). CONCLUSION: HPV16/18/45 accounted for two-thirds of the HPV types found in invasive cervical cancer in Mali and Senegal. Our results suggest that HIV may play a role in the underlying HPV18 and HPV45 contribution to cervical cancer, but further studies are needed to confirm this correlation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".