Are we ready for human papillomavirus testing?
Bibliographic record
Abstract
Objective To determine patient knowledge and preferences about primary human papillomavirus (HPV) testing. Design Cross-sectional survey. Setting Two family practice clinics (urban and suburban) and the social media platforms of 2 hospitals in the greater Toronto area between January and February 2023. Participants A total of 413 Ontario residents aged 25 to 69 years, with a cervix, who qualified for Papanicolaou (Pap) screening and could communicate in English. Methods Electronic survey containing questions about knowledge of, and preferences for, cervical cancer screening, including types of screening and screening intervals, and about education related to HPV and screening intervals. Main findings Of 441 potential participants, 426 were eligible and consented to participate in the study; ultimately 413 provided completed or partially completed surveys (96.9% response rate). Of those who completed a recent Pap test, 57.8% (208 of 360) knew of HPV testing. Initially, 27.8% thought HPV testing was better than Pap testing for cervical cancer screening. After learning HPV tests exist and have self-sampling options, most participants preferred HPV testing (self-sampling 46.3%, provider sampling 34.1%). Annual cervical cancer screening was preferred by 50.1% of participants despite knowing that, for most people, Pap tests should be conducted every 3 years (74.8%). After learning about HPV testing, participants were more likely to prefer 5-year screening intervals (43.8%); however, those in the family practice group were still more likely to prefer 3-year intervals compared with those in the social media group (P<.01). Conclusion Participants in this study identified a preference for HPV testing and self-sampling options. Concerns were raised about extended screening intervals and the safety of self-collected samples that need to be addressed in public health education initiatives during rollout of new screening programs.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".