The “Women’s Heart Bus”: the first french initiative for the prevention of cardiovascular and gynaecological risk in women, prospective multicentre analysis of 4,300 participants
Bibliographic record
Abstract
OBJECTIVE: To assess the cardiovascular and gynaecological health status of women in France, based on data collected through the Women's Heart Bus screening and prevention campaign. METHODS: A mobile screening campaign was carried out in 20 cities across France between September 2021 and November 2022. The initiative aimed to establish the cohort of the National Women's Health Observatory (ONSF) and included 4300 women. A structured assessment booklet was used to collect information on cardiovascular, metabolic, and gynaecological-obstetric risk factors, as well as on medical follow-up by general practitioners and cardiologists. Gynaecological screening practices were also analysed in relation to age and national screening guidelines. RESULTS: Among the women included, 90.2 % had at least two cardiovascular or metabolic risk factors, and 48.9 % had two or more gynaecological-obstetric risk factors. More than 70 % had never received cardiovascular follow-up, and fewer than half were up to date with gynaecological screening. CONCLUSION: The findings underscore the urgent need to improve cardiovascular and gynaecological health in women in France. The Women's Heart Bus enabled many women to be reintegrated into appropriate care pathways.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".