Bibliographic record
Abstract
High prevalence rate of human papillomavirus (HPV) has been one of the major health issues all over the world. Genital HPV is the most common sexually transmitted infection, and infects the skin and mucous membranes. Some HPV types can cause cervical cancer and other less common cancers, such as cancers of the vulva, vagina, anus, and penis. In this short review, current use and the latest trend in new technology development in HPV diagnostic methods were discussed. For cervical cancer diagnosis, Pap smear is employed as a preliminary test, and then one of nucleic acid detection methods such as Hybrid Capture ® 2 and PCR is followed for the positive cases. PCR technology which amplifies target HPV sequence is usually combined with various advanced detection methods. Several trials with real time PCR products have not been satisfactory yet. However, a good success was made with Seeplex ® HPV 18-plex and Amplico ® plate which adopt multiplex PCR technology. HPV DNA microarray chips have drawn a great interest due to its speedy procedure, comparable specificity and sensitivity, and its potential in high throughput application. The use of newly approved HPV microarray chips including 4 products in Korea and Papillocheck ® chip in EU, Japan and Canada will be expanded soon for practical diagnostic use.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".