Qualitative Study of the Role of Pap Screening on HPV Transmission Dynamics
Bibliographic record
Abstract
A new deterministic model is designed and used to assess the population-level impact of Pap cytology screening on the transmission dynamics of human papillomavirus (HPV), and associated dysplasia, in a community. In the absence of Pap screening, the disease-free equilibrium (DFE) of the resulting model is shown to be globally-asymptotically stable whenever the associated reproduction number (R0 ) is less than unity. Furthermore, the model has a unique endemic equilibrium, which is locally- and globally- asymptotically stable for special cases. The disease-free equilibrium of the Pap screening model is also shown to be globally-asymptotically stable when its reproduction number (R0s ) is less than unity. The effect of uncertainties in the estimates of the parameter values used in the numerica simulations of the Pap screening model is accounted for via uncertainty and sensitivity analysis. Numerical simulations of the Pap screening model show that HPV transmission models that do not incorporate disease transmission by individuals in the pre-cancerous stages may under-estimate the burden of HPV (and associated dysplasia) in the community. Although Pap screening significantly reduces the incidence of cervical cancer (for instance, detecting 50% of sexually-active females with cervical intraepithelial neoplasia resulted in 95% reduction of cervical cancer cases over 10 years), its singular use is insufficient to lead to the effective control of the spread of HPV in the community.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".