Cervical cancer screening pathways in France in 2015–2021, a nationwide study based on medico-administrative data
Bibliographic record
Abstract
To better document cervical cancer screening (CCS) pathways, the purpose of our study was to examine CCS pathways among women who had undergone a screening test (opportunistic or organised programme), based on real-life data over a 7-year period. This study used data from the French national health care database (SNDS), which covers almost 100 % of the French population of around 66 million inhabitants. Data from 2015 to 2021 were extracted. More than one quarter (27 %) of women who were at least 25 years old in 2015 and up to 65 years old in 2021 were not screened over the 2015-2021 period. Compared to women who had undergone screening at least once, women who were not screened were older (36 % vs. 23 % in the 50-59 years age group in 2015) and lived in the most deprived urban areas (21 % vs 16 % for less and most deprived respectively). 57 % of women underwent screening within recommended intervals, 13 % of women were under-screened, and 30 % were overscreened. Overall, our study identified that, in 2021, women who participated in the French organised screening programme were less likely to be screened within the recommended interval over the 7-year period. These analyses need to be continued over time in order to assess whether the programme helps reintegrate women into the screening process.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".