Predictors of High-grade Squamous Intraepithelial Lesion treatment failure
Bibliographic record
Abstract
Abstract Objective To estimate the association between several risk factors and high-grade squamous intraepithelial lesions (HSIL) treatment failure in order to identify predictors. Methods The study population included 1,548 Canadian women treated for HSIL who participated in a randomized control trial. HSIL treatment failure was the presence of histologically confirmed HSIL or worse during the two-year follow-up period. This nested-case control study included all 101 cases of treatment failure and controls that were matched 1:1 on treatment center and date of failure. Conditional logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) between each potential predictor and HSIL treatment failure. Independent variables that were examined included age, parity, smoking status, number of sexual partners, condom use, method of contraception, margins, number of passes, diagnosis on conisation, genotype, and number of infecting types. Interactions between smoking and margins and genotype were evaluated. Results Having positive vs. negative margins (adjusted OR=4.05, 95% CI 1.57-10.48) and being positive for Human Papillomavirus (HPV)16 and/or HPV18 vs. any other type (adjusted OR=2.69, 95% CI 1.32-5.49) were predictors of HSIL treatment failure in multivariable models. ORs suggested that older age, more severe lesions, and single-type infections may be at a higher risk of treatment failure but were not statistically significant. The ORs for smoking status, number of sexual partners, condom use, contraception, parity, and number of passes were near the null value. We did not observe any evidence of interaction between smoking and genotype, nor between margins and genotype. Conclusion Only positive margins and HPV16/18 positivity were predictors for being diagnosed with HSIL or worse within two years of treatment. However, we do not recommend automatic retreatment of those with positive margins because over 90% of those with positive margins did not fail treatment. The predictive value of HPV16 and HPV18 for HSIL treatment failure suggests that high coverage vaccination programs should contribute to a significant reduction in residual/recurrent disease.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".