Which women default from follow‐up cervical cytology tests? A cohort study within the TOMBOLA trial
Bibliographic record
Abstract
OBJECTIVE: To identify factors associated with default from follow-up cervical cytology tests. METHODS: A cohort study was conducted involving 2166 women, aged 20-59, with recent low-grade cervical cytology taken within the NHS Cervical Screening Programmes in Scotland and England, and managed by 6-monthly cytology in primary care. For the first (6-month) and second (12-month) surveillance cytology tests separately, women were categorized as 'on-time attendees' (attended ≤6 months of test being due), 'late attendees' (attended greater than 6 months after test was due) or 'non-attendees' (failed to attend). Multivariate odds ratios (ORs) were computed for factors associated with late and non-attendance. RESULTS: For the first surveillance test, risk of non-attendance was significantly higher in younger women, those without post-secondary education, and non-users of prescribed contraception. Factors significantly associated with late attendance for the first test were the same as for non-attendance, plus current smoking and having children. The most important predictor of non-attendance for the second surveillance test was late attendance for the first test (OR = 9.65; 95% CI, 6.60-16.62). Non-attendance for the second test was also significantly higher among women who were younger, smokers and had negative cytology on the first surveillance test. Late attendance for the second surveillance test was higher in women who were younger, smokers, had children and attended late for the first test. CONCLUSIONS: Women at highest risk of default from follow-up cytology tend to be young, smoke, lack post-secondary education, and have defaulted from a previous surveillance appointment. Tackling default will require development of targeted strategies to encourage attendance and research to better understand the reasons underpinning default.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".