Detection and Typing of Human Papillomavirus Nucleic Acids in Biological Fluids
Bibliographic record
Abstract
Human papillomaviruses (HPV) are the etiologic agents of cancer of the uterine cervix and several other neoplasias. Detection of HPV infection will improve the sensitivity of primary and secondary screening of cervical cancer. The clinical indications for the use of HPV tests will have to consider the natural history of HPV infection and diseases, and the multiplicity of types involved. Signal amplification HPV DNA tests detect several high-risk HPV types, are standardized, commercially available and approved for clinical use. Nucleic acid amplification techniques are ideal methods for epidemiologic purposes since they minimize misclassification of HPV infection status and allow detection of infection with low viral burden. They are currently under evaluation for clinical use. PCR is the most widespread method for HPV typing, especially with the use of consensus primers and typing with reverse hybridization techniques. Novel promising HPV detection strategies are now proposed, such as HPV mRNA detection, and suspension or solid phase arrays. These novel techniques will have to be evaluated as stringently as actual assays in clinical studies. Although assays have been developed for the evaluation of viral load, viral integration and HPV polymorphism in molecular epidemiological studies, their role in clinical practice is not currently defined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".