Defining the genetic susceptibility to cervical neoplasia—A genome-wide association study
Bibliographic record
Abstract
A small percentage of women with cervical HPV infection progress to cervical neoplasia, and the risk factors determining progression are incompletely understood. We sought to define the genetic loci involved in cervical neoplasia and to assess its heritability using unbiased unrelated case/control statistical approaches. We demonstrated strong association of cervical neoplasia with risk and protective HLA haplotypes that are determined by the amino-acids carried at positions 13 and 71 in pocket 4 of HLA-DRB1 and position 156 in HLA-B. Furthermore, 36% (standard error 2.4%) of liability of HPV-associated cervical pre-cancer and cancer is determined by common genetic variants. Women in the highest 10% of genetic risk scores have approximately >7.1% risk, and those in the highest 5% have approximately >21.6% risk, of developing cervical neoplasia. Future studies should examine genetic risk prediction in assessing the risk of cervical neoplasia further, in combination with other screening methods.
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 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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".