PS1-25: Cervical Cancer Testing and Follow-up in Four Managed Care Plans, 1998-2007
Bibliographic record
Abstract
BACKGROUND/AIMS: Cervical cancer screening is performed to detect pre-cancerous cervical intraepithelial neoplasia or invasive cancerous cervical lesions prior to the onset of symptoms so they can be removed before the cancer has developed or spread. With the addition of high-risk human papillomavirus (HPV) testing to the long-established Papanicolaou (Pap) smear, national and health plan screening guidelines have been regularly updated in recent years. Guidelines on screening frequency and follow-up protocols have also changed over time and will likely continue to do so as HPV vaccination becomes more widespread. Here we describe patterns and results of cervical cancer testing and follow-up over a 10-year period within four geographically-dispersed U.S. managed care organizations. METHODS: Using data collected by the SEARCH: Screening Effectiveness And Research in Community-Based Healthcare project, we analyzed electronic medical record data on all women aged 20–65 during the period 1998–2007 across four HMORN sites. We created standardized files for Pap smear dates and results; cervical histology dates, types, and results; and HPV test dates and results. We also collected Virtual Data Warehouse data on HPV vaccinations, and selected diagnosis and procedure codes. We calculated rates of Pap testing, HPV testing, colposcopy, and cervical histology (biopsy and treatment). We also calculated rates for Pap testing we classified as “screening.” Among women who had Pap tests in 2002 and 2007 and no abnormal test directly preceding the index test, we examined patterns of screening frequency. We also examined frequencies and trends in the results of “screening” Pap testing and cervical histology. RESULTS: Overall, annual Pap testing rates decreased and HPV testing rates dramatically increased over the 10 years, while rates of colposcopy, cervical histology, and cervical treatment did not display obvious patterns. Trends varied by age group and health plan. Pap screening frequency differed by health plan; overall, in 2007 a higher proportion of subjects had longer screening intervals (2 years or greater) than in 2002. Information on patterns of Pap and histology results will be presented. DISCUSSION: Evaluating trends in cervical cancer testing and follow-up may highlight opportunities to optimize cervical cancer screening delivery in community-based settings.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".