Potential impact of nonavalent <scp>HPV</scp> vaccine in the prevention of high‐grade cervical lesions and cervical cancer in Portugal
Bibliographic record
Abstract
OBJECTIVE: To estimate the potential impact of the nonavalent HPV vaccine for high-grade cervical lesions and invasive cervical cancer (ICC) in Portugal. METHODS: The present secondary analysis used data collected in the CLEOPATRE II study on the prevalence of HPV 6/11/16/18/31/33/45/52/58 among female patients aged 20-88 years. The prevalence of HPV types in patients with cervical intraepithelial neoplasia (CIN) grades 2/3 and ICC was examined. RESULTS: Data were included from 582 patients. There were 177, 341, and 64 patients with CIN2, CIN3, and ICC, respectively, and 169 (95.5%), 339 (99.4%), and 62 (96.9) of them had HPV infections. Of patients with HPV infections, HPV 16, 18, 31, 33, 45, 52, and 58 infections were identified in 150 (88.8%), 329 (97.1%), and 60 (96.8%) patients with CIN2, CIN3, and ICC, respectively. HPV genotypes 6, 11, 16, 18, 31, 33, 45, 52, and 58 were identified in 540 (94.7%) of the patients with HPV infections. CONCLUSION: The addition of the five HPV genotypes included in the nonavalent HPV vaccine (HPV 31/33/45/52/58) could result in the new HPV vaccine preventing 94.7% of CIN2/3 and ICC occurrences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".