The Laboratory Diagnosis of Genital Human Papillomavirus Infections
Bibliographic record
Abstract
Human papillomaviruses (HPVs) are the etiological agents of several genital cancers, including cancer of the uterine cervix. The detection of HPV infection in genital samples may increase the sensitivity of primary and secondary screenings of cervical cancer. HPV testing may also improve the specificity of screening programs, resulting in the avoidance of overtreatment and cost savings for confirmatory procedures. The major determinants of clinical progression of HPV infection include persistence of HPV infection, involvement of high-risk HPV types, high HPV viral load, integration of viral DNA and presence of several potential cofactors. Signal amplification HPV-DNA detection techniques (Hybrid Capture II, Digene Corporation, USA) are standardized, commercially available, and capable of detecting several high-risk HPV types. They also increase the sensitivity of screening for high-grade lesions in combination with cytology. The sensitivity of these techniques to detect high-grade lesions is higher than that of cytology, but the referral rate for colposcopy is greater. These techniques are approved for the triage to colposcopy of women with cervical smears interpreted as atypical squamous cells of undetermined significance. Triage and screening for cervical cancer using HPV will probably be restricted to women aged 30 years or older because of the high prevalence of infection in younger women. Amplification techniques are ideal for epidemiological studies because they minimize the misclassification of HPV infection status. These techniques can detect low HPV burden infections. Consensus primers amplify most genital types in one reaction, and the reverse hybridization of amplicons with type-specific probes allows for the typing of HPV-positive samples. Consensus PCR assays are currently under evaluation for diagnostic purposes. HPV testing is currently implemented for the clinical management of women.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".