Human papillomavirus detection in cervical neoplasia attributed to 12 high-risk human papillomavirus genotypes by region
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: We estimated the proportion of cervical intraepithelial neoplasia (CIN) cases attributed to 14 HPV types, including quadrivalent (qHPV) (6/11/16/18) and 9-valent (9vHPV) (6/11/16/18/31/33/45/52/58) vaccine types, by region METHODS: Women ages 15-26 and 24-45 years from 5 regions were enrolled in qHPV vaccine clinical trials. Among 10,706 women (placebo arms), 1539 CIN1, 945 CIN2/3, and 24 adenocarcinoma in situ (AIS) cases were diagnosed by pathology panel consensus. RESULTS: Predominant HPV types were 16/51/52/56 (anogenital infection), 16/39/51/52/56 (CIN1), and 16/31/52/58 (CIN2/3). In regions with largest sample sizes, minimal regional variation was observed in 9vHPV type prevalence in CIN1 (~50%) and CIN2/3 (81-85%). Types 31/33/45/52/58 accounted for 25-30% of CIN1 in Latin America and Europe, but 14-18% in North America and Asia. Types 31/33/45/52/58 accounted for 33-38% of CIN2/3 in Latin America (younger women), Europe, and Asia, but 17-18% of CIN2/3 in Latin America (older women) and North America. Non-vaccine HPV types 35/39/51/56/59 had similar or higher prevalence than qHPV types in CIN1 and were attributed to 2-11% of CIN2/3. CONCLUSIONS: The 9vHPV vaccine could potentially prevent the majority of CIN1-3, irrespective of geographic region. Notwithstanding, non-vaccine types 35/39/51/56/59 may still be responsible for some CIN1, and to a lesser extent CIN2/3.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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 it