Toward using National Cancer Surveillance data for preventing and controlling cervical and other human papillomavirus-associated cancers in the US
Bibliographic record
Abstract
T his supplement, known as the ABHACUS (Assessing the Burden of HPV-Associated Cancers in the United States) supplement, contains 22 articles.Together, these articles provide a comprehensive snapshot of data related to the occurrence and control of multiple cancers that have been associated with the human papillomavirus (HPV).These analyses highlight the burden of HPVassociated cancers in the US population as a whole and among vulnerable population subgroups.We anticipate that these findings will be an important resource for enhancing existing strategies for the prevention and control of HPV-associated cancers.HPV is estimated to be responsible for 5.2% of the cancers diagnosed worldwide.1 Virtually 100% of cervical cancers are causally associated with HPV, and there is increasing evidence of the role that HPV plays in other anogenital cancers and oropharyngeal cancers.With the recent approval and recommendation of an HPV vaccine that contains HPV-16 and HPV-18, interest in quantifying the Funded by the Centers for Disease Control and Prevention.This supplement to CANCER was supported by Cooperative Agreement Number U50 DP424071-04 from the Centers for Disease Control and Prevention (CDC).Dr. Ahmed was formerly with the Division of Cancer Prevention and Control at the Centers for Disease Control and Prevention, Atlanta, Georgia.The following registries, which cover approximately 83% of the US population, contributed data to the production of this supplement: Alaska, Alabama, Arkansas, California, Colorado, Connecticut, District of Columbia, Delaware, Florida, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky
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 | 0.036 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".