Selected class I and class II HLA alleles and haplotypes and risk of high‐grade cervical intraepithelial neoplasia
Bibliographic record
Abstract
Human leukocyte antigens (HLAs) present foreign antigens to the immune system and may be important determinants of cervical neoplasia. Previously published associations between HLA and cervical neoplasia exhibit considerable variation in findings. The biomarkers of cervical cancer risk (BCCR) case-control study addressed the role of specific HLA alleles as cofactors in the development of high-grade cervical intraepithelial neoplasia (HG-CIN) based on the most consistent evidence from published literature. Cases (N = 381) were women with histologically-confirmed HG-CIN attending colposcopy clinics and controls (N = 884) were women from outpatient clinics with normal cytological screening smears. Subjects were mainly of French-Canadian descent. Cervical specimens were tested for human papillomavirus (HPV) DNA and HLA genotypes by PGMY L1 consensus primer PCR and a PCR sequence-specific primer method, respectively. Unlike other studies, the DQB1*03 and DRB1*13 allele groups were not associated with risk of HG-CIN. The B7-DRB1*1501-DQB1*0602 haplotype was associated with a 41% overall reduction in HG-CIN risk (odds ratio [OR] = 0.59; 95% confidence interval [CI]: 0.36-0.96), and an 83% reduction in risk of HG-CIN among HPV 16 or HPV 18-positive subjects (OR = 0.17; 95%CI: 0.05-0.54). Paradoxically, however, the same haplotype was associated with HPV 16/18 infection risk among controls (OR = 8.44, 95%CI: 1.12-63.73). In conclusion, the B7-DRB1*1501-DQB1*0602 haplotype was protective against HG-CIN, especially in individuals infected with oncogenic HPV, but the mechanism of the association seems to involve multiple steps in the natural history of HPV and CIN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".