Epidemiologic Approaches to Evaluating the Potential for Human Papillomavirus Type Replacement Postvaccination
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
Currently, 2 vaccines exist that prevent infection by the genotypes of human papillomavirus (HPV) responsible for approximately 70% of cervical cancer cases worldwide. Although vaccination is expected to reduce the prevalence of these HPV types, there is concern about the effect this could have on the distribution of other oncogenic types. According to basic ecological principles, if competition exists between ≥2 different HPV types for niche occupation during natural infection, elimination of 1 type may lead to an increase in other type(s). Here, we discuss this issue of "type replacement" and present different epidemiologic approaches for evaluation of HPV type competition. Briefly, these approaches involve: 1) calculation of the expected frequency of coinfection under independence between HPV types for comparison with observed frequency; 2) construction of hierarchical logistic regression models for each vaccine-targeted type; and 3) construction of Kaplan-Meier curves and Cox models to evaluate sequential acquisition and clearance of HPV types according to baseline HPV status. We also discuss a related issue concerning diagnostic artifacts arising when multiple HPV types are present in specific samples (due to the inability of broad-spectrum assays to detect certain types present in lower concentrations). This may result in an apparent increase in previously undetected types postvaccination.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it