Human Papillomavirus Genotype Distribution among Cervical Cancer Patients prior to Brazilian National HPV Immunization Program
Bibliographic record
Abstract
To evaluate the impact of HPV immunization and possible changes in virus type-specific prevalence associated with cervical cancer, it is important to obtain baseline information based on socioeconomic, educational, and environmental characteristics in human populations. We describe these characteristics and the type-specific HPV distribution in 1,183 women diagnosed with cervical cancer in two Brazilian healthcare institutions located at the Southeastern (Rio de Janeiro/RJ) and the Amazonian (Belém/PA) regions. Large differences were observed between women in these regions regarding economic, educational, and reproductive characteristics. The eight most frequent HPV types found in tumor samples were the following: 16, 18, 31, 33, 35, 45, 52, and 58. Some HPV types classified as unknown or low risk were found in tumor samples with single infections, HPV 83 in RJ and HPV 11, 61, and 69 in PA. The proportion of squamous cervical cancer was lower in RJ than in PA (76.3% versus 87.3%,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn fontstyle="italic">0.001</mml:mn></mml:math>). Adenocarcinoma was more frequent in RJ than in PA (13.5% versus 6.9%,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn fontstyle="italic">0.001</mml:mn></mml:math>). The frequency of HPV 16 in PA was higher in younger women (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn fontstyle="italic">0.05</mml:mn></mml:math>). The success of a cervical cancer control program should consider HPV types, local health system organization, and sociodemographic diversity of Brazilian regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".