Effectiveness of Artificial Intelligence–Assisted Decision-making to Improve Vulnerable Women’s Participation in Cervical Cancer Screening in France: Protocol for a Cluster Randomized Controlled Trial (AppDate-You)
Bibliographic record
Abstract
BACKGROUND: The French organized population-based cervical cancer screening (CCS) program transitioned from a cytology-based to a human papillomavirus (HPV)-based screening strategy in August 2020. HPV testing is offered every 5 years, starting at the age of 30 years. In the new program, women are invited to undergo an HPV test at a gynecologist's, primary care physician's, or midwife's office, a private clinic or health center, family planning center, or hospital. HPV self-sampling (HPVss) was also made available as an additional approach. However, French studies reported that less than 20% of noncompliant women performed vaginal self-sampling when a kit was sent to their home. Women with lower income and educational levels participate less in CCS. Lack of information about the disease and the benefits of CCS were reported as one of the major barriers among noncompliant women. This barrier could be addressed by overcoming disparities in HPV- and cervical cancer-related knowledge and perceptions about CCS. OBJECTIVE: This study aimed to assess the effectiveness of a chatbot-based decision aid to improve women's participation in the HPVss detection-based CCS care pathway. METHODS: AppDate-You is a 2-arm cluster randomized controlled trial (cRCT) nested within the French organized CCS program. Eligible women are those aged 30-65 years who have not been screened for CC for more than 4 years and live in the disadvantaged clusters in the Occitanie Region, France. In total, 32 clusters will be allocated to the intervention and control arms, 16 in each arm (approximately 4000 women). Eligible women living in randomly selected disadvantaged clusters will be identified using the Regional Cancer Screening Coordinating Centre of Occitanie (CRCDC-OC) database. Women in the experimental group will receive screening reminder letters and HPVss kits, combined with access to a chatbot-based decision aid tailored to women with lower education attainment. Women in the control group will receive the reminder letters and HPVss kits (standard of care). The CRCDC-OC database will be used to check trial progress and assess the intervention's impact. The trial has 2 primary outcomes: (1) the proportion of screening participation within 12 months among women recalled for CCS and (2) the proportion of HPVss-positive women who are "well-managed" as stipulated in the French guidelines. RESULTS: To date, the AppDate-You study group is preparing and developing the chatbot-based decision aid (intervention). The cRCT will be conducted once the decision aid has been completed and validated. Recruitment of women is expected to begin in January 2023. CONCLUSIONS: This study is the first to evaluate the impact of a chatbot-based decision aid to promote the CCS program and increase its performance. The study results will inform policy makers and health professionals as well as the research community. TRIAL REGISTRATION: ClinicalTrials.gov NCT05286034; https://clinicaltrials.gov/ct2/show/NCT05286034. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/39288.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.005 |
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".