Clinical application of DNA ploidy to cervical cancer screening: A review
Bibliographic record
Abstract
Screening for cervical cancer with DNA ploidy assessment by automated quantitative image cytometry has spread throughout China over the past decade and now an estimated 1 million tests per year are done there. Compared to conventional liquid based cytology, DNA ploidy has competitive accuracy with much higher throughput per technician. DNA ploidy has the enormous advantage that it is an objective technology that can be taught in typically 2 or 3 wk, unlike qualitative cytology, and so it can enable screening in places that lack sufficient qualified cytotechnologists and cytopathologists for conventional cytology. Most papers on experience with application of the technology to cervical cancer screening over the past decade were published in the Chinese language. This review aims to provide a consistent framework for analysis of screening data and to summarize some of the work published from 2005 to the end of 2013. Of particular interest are a few studies comparing DNA ploidy with testing for high risk human papilloma virus (hrHPV) which suggest that DNA ploidy is at least equivalent, easier and less expensive than hrHPV testing. There may also be patient management benefits to combining hrHPV testing with DNA ploidy. Some knowledge gaps are identified and some suggestions are made for future research directions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| 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) | 0.003 | 0.001 |
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".