Human Papillomavirus Typing and Viral Gene Expression Analysis for the Triage of Women With Abnormal Results From Papanicolaou Test Smears to Colposcopy
Bibliographic record
Abstract
Abstract Context. —A cascade of molecular tests for human papillomavirus (HPV), as a follow-up to Papanicolaou test screening, could eliminate unnecessary colposcopy. Tests based on detection of HPV E6 messenger RNA (mRNA) are already being used as screening tools, but there is a good biological rationale for expecting that an increase in the relative amounts of HPV E6 mRNA in cervical samples may better predict cancerous transformation. Objective. —To compare some of the available diagnostic methods and our novel method of relative quantification (RQ) of HPV gene expression for the effective triage of women with abnormal results from Papanicolaou tests to colposcopy. Design. —Sensitivities, specificities, and likelihood ratios were calculated for repeat Papanicolaou test smears, HPV DNA polymerase chain reactions, HPV genotyping, HPV-16 E6 mRNA detection, and the RQ of HPV-16 E6 mRNA calibrated to cellular RNA and DNA levels and standardized to viral load. Results. —Human papillomavirus genotype in combination with a repeat Papanicolaou test can be used to categorize most women (96%) with cervical intraepithelial neoplasia of grade 2 or higher for colposcopy while eliminating 44% of women with cervical intraepithelial neoplasia 1 or less. The presence of HPV-16 E6 mRNA ( P < .001) and RQ of HPV-16 E6 mRNA ( P < .001) displayed significant median differences among the various grades of cervical intraepithelial neoplasia. Further testing of women who are positive for HPV-16 demonstrated that the RQ of E6 mRNA has diagnostic potential when combined with Papanicolaou testing in populations with higher disease prevalence. Conclusions. —The RQ of HPV E6 mRNA and HPV genotype could be useful in a cascade of diagnostic testing designed to refer women with findings of cervical abnormalities for colposcopy or treatment while reducing triage numbers.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".