Harms of cervical cancer screening in the United States and the Netherlands
Bibliographic record
Abstract
We studied harms related to cervical cancer screening and management of screen-positive women in the United States (US) and the Netherlands. We utilized data from four US integrated health care systems (SEARCH), the US National Health Interview Survey, New Mexico state, the Netherlands national histopathology registry, and included studies on adverse health effects of cervical screening. We compared the number of Papanicolaou (Pap) smear tests, abnormal test results, punch biopsies, treatments, health problems (anxiety, pain, bleeding and discharge) and preterm births associated with excisional treatments. Results were age-standardized to the 2007 US population. Based on SEARCH, an estimated 36 million Pap tests were performed in 2007 for 91 million US women aged 21-65 years, leading to 2.3 million abnormal Pap tests, 1.5 million punch biopsies, 0.3 million treatments for precancerous lesions, 5 thousand preterm births and over 8 million health problems. Under the Netherlands screening practice, fewer Pap tests (58%), abnormal test results (64%), punch biopsies (75%), treatment procedures (40%), preterm births (60%) and health problems (63%) would have occurred. The SEARCH data did not differ much from other US data for 2007 or from more recent data up to 2013. Thus compared to the less intensive screening practice in the Netherlands, US practice of cervical cancer screening may have resulted in two- to threefold higher harms, while the effects on cervical cancer incidence and mortality are similar. The results are also of high relevance in making recommendations for HPV screening. Systematic collection of harms data is needed for monitoring and for better incorporation of harms in making screening recommendations.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".