Naturally developed HPV16 antibodies and risk of newly detected cervical HPV infection outcomes
Bibliographic record
Abstract
Little is known about the protection conferred by antibodies from natural human papillomavirus (HPV) infection. Our objective was to evaluate the association between HPV16 seroreactivity and HPV16 redetection, newly detected HPV infections, and loss of HPV DNA detection during follow-up. We analyzed data from 2462 unvaccinated Brazilian women. HPV16 IgG and neutralizing antibodies at baseline were assessed by enzyme-linked immunosorbent assay (n = 1975) and by the pseudovirus-based papillomavirus neutralization assay (n = 487). HPV detection, genotyping, and viral load were assessed by PCR-based methods. The associations were analyzed by Cox proportional hazards models. We observed a positive association between HPV16 IgG seroreactivity and redetection of HPV16 infections. Age-adjusted hazard ratios (HR) with 95% confidence intervals (CI) ranged from 2.45 (1.04-5.74) to 5.10 (1.37-19.00). Positive associations were also observed between HPV16 IgG antibodies and (1) newly detected HPV infections by genotypes unrelated to HPV16 (age-adjusted HR [95% CI] = 1.32 [1.08-1.2]) and (2) loss of detection of HPV infections by genotypes unrelated to HPV16 (age-adjusted HR [95% CI] = 1.24 [1.03-1.50]). Naturally developed HPV16 antibodies do not prevent recurrent HPV infections. Overall HPV16 IgG and neutralizing antibodies seem to be serological markers for latent or past infections.
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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 | 0.004 |
| 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 | 0.000 |
| 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 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".