Lack of Agreement Between Cervicography and Cytology and the Effect of Human Papillomavirus Infection and Viral Load
Bibliographic record
Abstract
OBJECTIVE: Cohort studies of the natural history of human papillomavirus (HPV) infection and cervical squamous intraepithelial lesions with repeated screening allow the comparison of different macroscopic and microscopic diagnostic methods. MATERIALS AND METHODS: Concurrent visual inspection using cervicography and conventional Pap cytology tests were performed during multiple visits in a cohort of women attending a maternal and child health clinic in São Paulo, Brazil. HPV infection status at the same visits was determined by polymerase chain reaction followed by typing with specific oligonucleotide probing and viral load quantification. Information on reproductive health and hygiene habits was also collected at each visit. RESULTS: Overall agreement between cervicography and cytology was low (kappa = 0.046), which increased only slightly when high oncogenic-risk HPV types (kappa = 0.120) or high viral burden (>100 copies/cell) (kappa = 0.170) was present. Analysis of reproductive health and hygiene habits revealed somewhat different risk factors for cervical lesions detected by these tests. However, presence of oncogenic HPV DNA (odds ratio = 36.0, 95% CI = 16.6-77.8) and high viral burden (odds ratio = 67.34; 95% CI = 27.1-167.0) were strongly associated with lesions detected by cytology but not by cervicography. CONCLUSIONS: Although changes in the cervix (because of age, gravidity, or hormonal effects) may influence the performance of morphology-based screening tests, the lack of agreement and the different degrees of association with HPV infection measures indicate that a visual inspection method such as cervicography may detect different cervical abnormalities relative to cytology.
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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 | 0.029 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".