Risk of Human Papillomavirus (HPV) Infection and Cervical Neoplasia after Pregnancy
Bibliographic record
Abstract
BACKGROUND: Parity is well established as a risk factor for cervical cancer. It is not clear, however, how pregnancy influences the natural history of HPV infection and cervical neoplasia. Our objective was to study the risk of HPV infection and cervical squamous intraepithelial lesions (SIL) after pregnancy. METHODS: We used the Ludwig-McGill cohort study which includes 2462 women recruited in Sao Paulo, Brazil in 1993-97 and followed for up to 10 years. Cellular specimens were collected every 4-6 months for Pap cytology and HPV detection and genotyping by a polymerase chain reaction protocol. Study nurses recorded pregnancy occurrence during follow-up. HPV and Pap results from pregnant women were available before and after, but not during pregnancy. The associations between pregnancy and post-partum HPV infection/SIL were studied using generalized estimating equation models with logistic link. Adjusted odds ratios (OR) were estimated with empirical adjustment for confounding. RESULTS: We recorded 122 women with a history of pregnancy during follow-up. Of these, 29 reintegrated the cohort study after delivery. No association between HPV and pregnancy was found. A single SIL case (high grade SIL) occurred post-partum. Likewise, there was no association between pregnancy and risk of low grade SIL or any-grade SIL at the next visit (adjusted OR = 0.84, 95 % CI: 0.46-15.33) after controlling for confounders. CONCLUSIONS: No associations were found between pregnancy and HPV or LSIL. The single observed case of HSIL post-partum was more than would be expected based on the rate of these abnormalities among non-pregnant women. As this association was found with only one case, caution is required in the interpretation of these results.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".