Human papillomavirus‐based screening at extended intervals missed fewer cervical precancers than cytology in the <scp>HPV For Cervical</scp> Cancer (<scp>HPV FOCAL</scp>) trial
Bibliographic record
Abstract
While cervix screening using cytology is recommended at 2- to 3-year intervals, given the increased sensitivity of human papillomavirus (HPV)-based screening to detect precancer, HPV-based screening is recommended every 4- to 5-years. As organized cervix screening programs transition from cytology to HPV-based screening with extended intervals, there is some concern that cancers will be missed between screens. Participants in HPV FOr CervicAL Cancer (HPV FOCAL) trial received cytology (Cytology Arm) at 24-month intervals or HPV-based screening (HPV Arm) at 48-month intervals; both arms received co-testing (cytology and HPV testing) at exit. We investigated the results of the co-test to identify participants with cervical intraepithelial neoplasia grade 2 or higher (CIN2+) who would not have had their precancer detected if they had only their arm's respective primary screen. In the Cytology Arm, 25/62 (40.3%) identified CIN2+s were missed by primary screen (ie, normal cytology/positive HPV test) and all 25 had normal cytology at the prior 24-month screen. In the HPV arm, three CIN2+s (3/49, 6.1%) were missed by primary screen (ie, negative HPV test/abnormal cytology). One of these three misses had low-grade cytology findings and would also not have been referred to colposcopy outside of the trial. Multiple rounds of cytology did not detect some precancerous lesions detected with one round of HPV-based screening. In our population, cytology missed more CIN2+, even at shorter screening intervals, than HPV-based screening. This assuages concerns about missed detection postimplementation of an extended interval HPV-based screening program. We recommend that policymakers consider a shift from cytology to HPV-based cervix screening.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".