Multiparameter Calibration of a Natural History Model of Cervical Cancer
Bibliographic record
Abstract
The objective of this study was to develop a comprehensive natural history model of human papillomavirus (HPV) and cervical cancer using a two-step approach to model calibration. In the first step, the authors utilized primary epidemiologic data from a longitudinal study of women in Brazil and identified a plausible range for each input parameter that produced model output within the 95% confidence intervals of the data. In the second step, they performed a simultaneous search over all input parameters to identify parameter sets that produced output consistent with data from multiple sources. A goodness-of-fit score was computed for 555,000 unique parameter sets using a likelihood-based approach, and a sample of good-fitting parameter sets was used in the model to illustrate the advantage of the calibration approach by projecting a range of benefits associated with cervical cancer prevention policies. The calibrated model had reasonable fit to the data in terms of duration and prevalence of HPV infection for high-risk types, prevalence of precancerous lesions, and incidence of cancer. The authors found that leveraging primary data from longitudinal studies provides unique opportunities for model parameterization of the unobservable nature of HPV infection and its role in the development of cervical cancer.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".