Approaches to Estimating Clearance Rates for Human Papillomavirus Groupings: A Systematic Review and Real Data Examples
Bibliographic record
Abstract
INTRODUCTION: Approaches to estimating clearance rates, an important metric of human papillomavirus (HPV) clearance, for HPV groupings differ between studies. We aimed to identify the approaches used in the literature for estimating grouped HPV clearance rates. We investigated whether these approaches resulted in different estimations, using data from existing studies. METHODS: In this systematic review, we included articles that reported clearance rates of HPV groupings. We identified approaches to data in the HAVANA cohort, comprising adolescent girls, and the H2M cohort, comprising men who have sex with men. We estimated clearance rates for six HPV groupings (bivalent-, quadrivalent- and nonavalent vaccine-related, and low-risk, high-risk, and any HPV). RESULTS: From 26 articles, we identified 54 theoretically possible approaches to estimating clearance rates. These approaches varied regarding definitions of clearance events and person-time, and prevalence or incidence of infections included in the analysis. Applying the nine most-used approaches to the HAVANA ( n = 1,394) and H2M ( n = 745) cohorts demonstrated strong variation in clearance rate estimates depending on the approach used. For example, for grouped high-risk HPV in the H2M cohort, clearance rates ranged from 52.4 to 120.0 clearances/1000 person-months. Clearance rates also varied in the HAVANA cohort, but differences were less pronounced, ranging from 24.1 to 57.7 clearances/1000 person-months. CONCLUSIONS: Varied approaches from the literature for estimating clearance rates of HPV groupings yielded different clearance rate estimates in our data examples. Estimates also varied between study populations. We advise clear reporting of methodology and urge caution in comparing clearance rates between studies.
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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 | 0.100 | 0.376 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".