Vaccine Efficacy Against HPV 16 and 18
Bibliographic record
Abstract
There have been safe vaccines for almost two decades to offer protection against human papilloma virus (HPV), a common viral sexually transmitted infection. In some cases, HPV 16 and 18 in particular can cause cervical, anal, penile, oropharyngeal, vaginal, and vulvar cancers. Vaccines could eradicate the vast majority of HPV-related cancer, but currently, there is not nearly enough utilization, especially in low- and middle-income countries (LMICs). An easier HPV vaccine dose schedule would benefit LMICs, in particular, and several recent studies have foreshadowed some possibilities. For example, the Costa Rica Vaccine Trial supported the efficacy of three doses of the bivalent HPV vaccine. A Canadian study found that two doses of the quadrivalent HPV vaccine and two doses plus a booster shot five years later produced similar results. A subsequent analysis of the Costa Rica Vaccine Trial studied participants who received either one dose, two doses, or three doses, and despite fewer detectable antibodies, a single dose against HPV 16 and 18 demonstrates similar protection to two and three doses. Likewise, an Indian trial underscores that one dose of the quadrivalent HPV vaccine is effective. Additionally, an ongoing study presents that Cecolin, a more affordable option, is non-inferior to the quadrivalent vaccine. Taken together, these results signal that a single dose of the HPV vaccine may be sufficient, which may prompt improved vaccination plans for LMICs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".