Systematic review and meta-analysis of the association between naturally induced IgG, IgM and neutralising antibodies to HPV16 and newly detected cervical HPV16 infection outcomes
Bibliographic record
Abstract
Background It is unclear whether recurrent cervical human papillomavirus type 16 (HPV16) infections can be prevented by naturally induced HPV16 antibodies in unvaccinated healthy women. Methods We systematically searched the literature for studies that prospectively evaluated the association between HPV16 naturally induced IgG, IgM, and neutralising antibodies and newly detected cervical HPV16 infection in unvaccinated women. Data were quantitatively summarised by random effect meta-analysis. Results Naturally induced HPV16 IgG and neutralising antibodies were negatively associated with newly detected HPV16 infection (relative risk (RR) (95% confidence interval (CI))=0.71 (0.63 to 0.80) and 0.54 (0.36 to 0.73), respectively). HPV16 antibodies tend to offer protection against subsequent HPV16 DNA detection in young women (RR (95% CI)=0.65 (0.55 to 0.74)), but not in women aged over 25 years (RR (95% CI)=0.88 (0.73 to 1.04)). HPV16 IgG antibodies were also negatively associated with persistent HPV16 infection (adjusted RR=0.67 (0.56 to 0.78)). There was high heterogeneity between studies (I2statistic=63.9%; p=0.007), and most had low risk of bias. We did not find studies evaluating IgM antibodies. Conclusion Seroreactivity to HPV16 infection seems to provide moderate protection against newly detected cervical HPV16 infection outcomes in unvaccinated women. However, protection seems to be affected by age. These findings should be considered when evaluating public health interventions against HPV. PROSPERO registration number CRD42022339579.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".