Bibliographic record
Abstract
Castle observes that a cohort of women in their 20s will demonstrate greater high-grade cervical disease rates than the same cohort during their teenage years. These observations do not explain why a cohort of Australian women in their 20s, after the introduction of human papillomavirus (HPV) vaccination, demonstrated greater high-grade cervical disease rates than a cohort of Australian women in their 20s before the introduction of HPV vaccination. We questioned whether the Australian observations support concerns previously expressed by the US Food and Drug Administration about the potential for HPV vaccination to enhance disease among subgroups of vaccinated individuals. Cancer prevention is not an established benefit of HPV vaccination. Cervical cancer prevention is an established benefit of Papanicolaou screening. The introduction of Papanicolaou screening to Vietnam ( 1 ) was associated with 50% reductions in cervical cancer incidence within 5 years ( 2 ). Castle previously acknowledged Papanicolaou screening in Vietnam to be a success ( 3 ). It is prudent to assume, until demonstrated otherwise, that the introduction of HPV vaccines to resource-constrained settings will divert resources from the development of cervical screening programs ( 2 ). The decision by the former Global Alliance for Vaccines and Immunization to promote HPV vaccination appears inconsistent with World Health Organization guidance that introduction of HPV vaccines should not divert resources from cervical screening programs ( 4 ). Perhaps the most important lesson learned in Vietnam is that cervical cancer prevention efforts are more effective when leaders embrace an ideological commitment to “improving health outcomes as rapidly as possible among as many people as possible” and assimilate the policy implications of that commitment ( 2 ). We invite Castle to incorporate this commitment into the mission statement of his Global Cancer Initiative, as we have done for our organization ( 5 ). The policy implications of this commitment include indispensable roles for Papanicolaou cytotechnology (even in HPV-based cervical screening programs) and a greatly diminished role for HPV vaccination in resource- constrained settings ( 2 ). Competing ideological commitments engender imprudent yet commercially useful alternative policies prone to decelerate global reductions in mortality ( 2 ).
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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 | 0.004 | 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".