Advances in human papillomavirus detection for cervical cancer screening and diagnosis: challenges of conventional methods and opportunities for emergent tools
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".