Response
Bibliographic record
Abstract
In our recent article in the Journal, we assumed that human papillomavirus (HPV) vaccines do not enhance or prevent disease among individuals infected by a vaccine type. Suba et al. question this assumption and suggest that there is evidence that HPV vaccination may enhance disease among subgroups of women infected with an HPV vaccine type. This is based on an exploratory subgroup analysis of study 013 for Gardasil ( 1 ) and an ecological analysis of administrative data from Australia ( 2 ). Exploratory subgroup analysis results from clinical trials must be interpreted with great caution because they are often underpowered and can be biased because randomization is not always effective at balancing baseline risk in smaller subgroups. These limitations are present in the subgroup analysis mentioned by Suba et al. First, in the 013 study, the observed negative vaccine efficacy against HPV6, -11, -16, and -18 cervical intraepithelial neoplasia 2/3 or worse (CIN2/3+) was not statistically significant (−44.6%,95% confidence interval = <0.0% to 8.5%) among subjects who were polymerase chain reaction positive and seropositive for the relevant HPV vaccine type at day 1 ( 1 ). If one assumes that the vaccine has no enhancing/therapeutic effect, one would expect the efficacy estimates from clinical trials to fluctuate around 0%. Thus, the negative empirically based estimate of efficacy is not surprising. However, the efficacy estimates should tend toward 0% with increasing sample size. To this effect, another clinical trial for Gardasil (study 015) showed a non-statistically significant positive efficacy against CIN2/3+ among individuals with prior evidence of infection ( 3 ). Second, the Gardasil group in study 013 had more baseline risk factors for the development of CIN2/3+ than the placebo group ( 1 ). For example, the baseline prevalence of high-grade cervical lesions was 1.86 times higher in the Gardasil group than the placebo group. Thus, it is not surprising that the Gardasil group had higher rates of CIN2/3+ during the trial ( 1 ). Ecological surveillance studies are prone to bias and, consequently, cannot be used to conclude that there is a causal link between an exposition and an outcome. Suba et al. use data from an ecological study from Australia showing statistically significant higher incidence of high-grade cervical abnormalities among older women in the years after vaccination (increase of 0.18% compared with prevaccination) to support the hypothesis that HPV vaccines can enhance disease ( 2 ). However, high-grade cervical abnormality incidence started to increase in the period of 2005 and 2006 (before vaccination in 2007), which coincides with statistically significant decreases in low-grade cervical abnormalities (decrease of 0.60% compared with prevaccination) among older women ( 2 ), increased participation in cervical cancer screening of higher-risk women due to targeted campaigns, and changes to screening guidelines in 2006 (personal communication, J. Brotherton). In conclusion, our modeling assumptions are based on reliable evidence. Furthermore, even if our model assumed that HPV vaccines enhance disease among individuals infected by a vaccine type, it would have no impact on the conclusions of our paper because the overwhelming majority of females are vaccinated before becoming sexually active or being exposed to HPV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.095 | 0.057 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".