HLA Polymorphisms and Cervical Human Papillomavirus Infection in a Cohort of Montreal University Students
Bibliographic record
Abstract
BACKGROUND: Only a minority of women with human papillomavirus (HPV) infection eventually develop cervical cancer, which suggests that host immune mechanisms play a role in the disease. HLA polymorphisms have been linked to the risk of cervical cancer, but very little is known about the role that they play in the acquisition and persistence of HPV infection. METHODS: A cohort study of cervical HPV infections was used to examine the role that 5 HLA alleles (B*07, DQB1*03, DQB1*0602, DRB1*13, and DRB1*1501) play in determining the risk of HPV positivity and persistence in 524 female university students in Montreal. HPV positivity was determined by use of the MY09/11 polymerase-chain-reaction protocol. HLA alleles from purified DNA from cervical specimens were typed by use of a polymerase-chain-reaction technique using sequence-specific primers. RESULTS: HLA DRB1*13 was associated with cumulative risk of HPV infections (odds ratio [OR], 1.7 [95% confidence interval {CI}, 1.0-2.8]), for oncogenic HPV (OR, 1.6 [95% CI, 0.9-2.8]), and for HPV-16 (OR, 2.0 [95% CI, 0.9-4.4]). DQB1*03 was consistently associated with a lower cumulative risk of HPV infections, but this association was not statistically significant. None of the alleles affected the risk of HPV persistence. CONCLUSIONS: The results of this study support the hypothesis that certain HLA class II polymorphisms mediate genetic susceptibility to the acquisition of HPV infection.
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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) | 0.003 | 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".