The Unintended Consequences of Cervical Screening
Bibliographic record
Abstract
OBJECTIVE: It is well known that receipt of an initial abnormal cervical cytology test can trigger considerable anxiety among women. Less is known about the impact of follow-up by repeat cytology tests. We quantified prevalence, and identified predictors, of distress after repeat cytologic testing in women with a single low-grade test. METHODS: Within the framework of the TOMBOLA randomized controlled trial of alternative managements, 844 women aged 20 to 59 years with a single routine cytology test showing borderline nuclear abnormalities (BNA; broadly equivalent to atypical squamous cells of undetermined significance) were assigned to follow-up by repeat cytology in primary care (the first test was due 6 months after the initial BNA result). Women completed sociodemographic and psychosocial questionnaires at recruitment and the Impact of Event Scale (IES) 6 weeks after their first follow-up cytology test. Factors associated with significant psychologic distress (IES ≥ 9) were identified using logistic regression. RESULTS: The response rate was 74% (n = 621/844). Of all the respondents, 39% scored in the range for significant distress. Distress varied by follow-up cytology result: negative, 36%; BNA or mild dyskaryosis, 42%; other (including high grade and inadequate), 55%. After adjusting for the cytology result, risk of distress was significantly raised in women who had significant anxiety at recruitment, reported experiencing pain after the follow-up cytology, had children, or were dissatisfied with support they had received after their initial BNA test. CONCLUSIONS: Substantial proportions of women experience surveillance-related psychologic distress after a follow-up cytology test, even when the result is negative. This is an important, albeit unintended, consequence of cervical screening. Strategies to alleviate this distress merit attention.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".