Advances in human papillomavirus detection for cervical cancer screening and diagnosis: challenges of conventional methods and opportunities for emergent tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human papillomavirus (HPV) infection is the main cause of cervical cancer and other cancers such as anogenital and oropharyngeal cancers. The prevention screening and treatment of cervical cancer has remained one of the top priorities of the World Health Organization (WHO). In 2020, the WHO came up with the 90-70-90 strategy aimed at eliminating cervical cancers as a public health problem by the year 2030. One of the key priorities of this strategy is the recommendation for countries to ensure that 70% of their women are screened using a high-performance test by the age of 35, and again by the age of 45. Over the years, several traditional methods (notably, Pap smear and nucleic acid-based techniques) have been used for the detection of cervical cancer. While these methods have significantly reduced the incidence of cervical cancer and death, they still come short of excellence for the total eradication of HPV infection. The challenges include low sensitivity, low specificity, poor reproducibility, the need for high-level specialists, and the high cost of access to the facilities, to mention a few. Interestingly, however, several efforts are being made today to mitigate these challenges. In this review, we discussed the pros and cons of the traditional screening and testing of HPV infections, the efforts being made to improve their performances, and the emergent tools (especially, the electrochemical methods) that promise to revolutionize the screening and testing of HPV infections. The main aim of the review is to provide some novel clues to researchers that would allow for the development of high-performance, affordable, and triage-suitable electrochemical-based diagnostic tools for HPV and cervical cancer.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it