What Are the Current HPV Types Contributing to Cervical High-Grade Squamous Intraepithelial Lesions, Adenocarcinoma In Situ, and Early Cervical Cancer?
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of human papillomavirus (HPV) types by genotyping high-grade squamous intraepithelial lesion (HSIL), adenocarcinoma in situ (AIS), and early-stage invasive cervical cancer (ICC) in patients who have been exposed or are naïve to the HPV vaccine. METHODS: This was a cross-sectional study. All patients over the age of 18 years who presented to the colposcopy clinic with HSIL, AIS, or ICC who were expected to undergo a cervical biopsy, loop electrosurgical excisional procedure, or cone biopsy were eligible and approached for informed consent. HPV typing was performed to identify the causative HPV types. RESULTS: Between November 2016 and May 2023, 113 patients (34 vaccinated with at least 1 dose, and 79 non-vaccinated) consented to this study. The median ages at coitarche and study entry were 18 (range 14-37) and 34 (range 24-66) years, respectively. Only 3 patients were vaccinated prior to coitarche. Histology was as follows: HSIL = 97, AIS = 9, HSIL and AIS = 2, squamous cell carcinoma = 4, and 1 patient with adenocarcinoma. The causative HPV type was 16 or 18 in 59% of the vaccinated group and in 66% of the non-vaccinated group. Most vaccinated patients (74%) reported receiving 2-3 doses of HPV vaccine. CONCLUSIONS: In our cohort, the distribution of causative HPV 16 and 18 in patients presenting with HSIL/AIS/ICC was similar between vaccine-naïve and vaccinated patients. This data suggests cervical screening guidelines should not differentiate between "vaccinated" and "non-vaccinated" women without further details of their vaccination.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".