Molecular variants of human papillomavirus types 16 and 18 preferentially associated with cervical neoplasia
Bibliographic record
Abstract
In order to determine geographically related intratypic variation in human papillomavirus (HPV) type 16 and 18 isolates that could be associated with lesion development, data were analysed from an ongoing cohort study of the natural course of infection of HPVs and cervical neoplasia. Testing for HPVs was carried out by PCR and molecular variants of these HPVs were characterized by sequence analysis of the long control region and by dot blot hybridization of the E6 and L1 genes. Tests for HPV were done in multiple first-year specimens from 1690 women enrolled in a cancer screening program from 1993 to 1997. Subjects were followed-up by cytology and cervicography for detection of cervical lesions. Seven variants of HPV-16 and four of HPV-18 were detected in one or more specimens from 65 subjects. The same variant was found in specimens taken on different visits from each case of persistent infection. Overall, non-European variants tended to persist more frequently [odds ratio (OR)=4.5; 95% confidence interval (CI), 1.6-12.4] than European (E) variants (OR=2.5; 95% CI, 1.3-4.9), relative to the risk of persistence for non-oncogenic HPVs. In addition, non-E variants were more strongly associated with risk of both prevalent (age- and race-adjusted OR=172.2; 95% CI, 47.1-630.1) and incident [relative risk (RR)=22.5; 95% CI, 6.0-83.9] high-grade lesions than E variants (prevalent lesions OR=46.3; 95% CI, 15.5-138.0 and incident lesons RR=6.1; 95% CI, 1.3-27.4), relative to the risk for HPV-negative women. Although consistent, the latter differences were not statistically significant. If confirmed in other populations, measurement of intratypic variation of HPV-16 and -18 has the potential to serve as an ancillary tool in cervical cancer screening.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".