Human Papillomavirus Infection and Time to Progression and Regression of Cervical Intraepithelial Neoplasia
Bibliographic record
Abstract
BACKGROUND: Little is known about the duration of precancerous cervical lesions in relation to human papillomavirus (HPV) infection. We estimated rates of progression and regression and sojourn times of cervical squamous intraepithelial lesions (SILs) according to HPV status. METHODS: We used data from a longitudinal study of HPV infection and cervical neoplasia in São Paulo, Brazil. Cervical specimens were taken from 2404 women for Pap cytology and polymerase chain reaction-based HPV testing every 4-6 months over a period of 8 years. We used actuarial and non-actuarial analyses to measure time to and rates of lesion progression and regression according to status and type of HPV infection. RESULTS: During follow-up, 118 low-grade SIL (LSIL), 24 high-grade SIL (HSIL), and 173 atypical squamous cells of undetermined significance (ASCUS) events were detected. Mean time to progression from ASCUS to LSIL or worse and from LSIL to HSIL or worse was shorter in women with oncogenic HPV types than in women with no HPV infection (mean times for ASCUS progression were 67.0 and 88.0 months, respectively, in women with oncogenic HPV and no HPV, difference = 21.0 months, 95% confidence interval [CI] = 11.3 to 30.7 months; mean times for LSIL progression were 73.3 and 83.5 months, respectively, difference = 10.2 months, 95% CI = -0.15 to 20.6 months). Half of the LSILs regressed to normal or ASCUS within 6 months. Mean times for regression from ASCUS to normal, from LSIL to ASCUS or normal, and from HSIL/cervical intraepithelial neoplasia 2 to ASCUS or normal were longer for women with oncogenic HPV types (16.8 months, 95% CI = 7.5 to 26.2 months; 13.8 months, 95% CI = 8.8 to 18.7 months; and 17.1 months, 95% CI = 4.1 to 30.1 months, respectively) than for women with non-oncogenic HPV types (7.7 months, 95% CI = 5.2 to 10.2 months; 7.8 months, 95% CI = 5.3 to 10.2 months; 8.9 months, 95% CI = 3.3 to 14.6 months) or for women with no HPV infection (7.6 months, 95% CI = 6.9 to 8.4 months; 7.6 months, 95% CI = 6.4 to 8.7 months; and 7.0 months, 95% CI = 5.0 to 8.9 months, respectively). CONCLUSION: Precursor lesions of the cervix persist longer and progress more quickly in women with oncogenic HPV infections than in women with non-oncogenic infections or without HPV. Testing cervical lesions for oncogenic HPVs may help identify those that are likely to progress rapidly.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".