Understanding Integrated Hpv Testing and Treatment of Pre- Cancerous Cervical Cancer in Burkina Faso, Cote D’Ivoire, Guatemala and Philippines: Study Protocol
Bibliographic record
Abstract
Abstract Background Many low- and-middle-income countries are disproportionately burdened by cervical cancer, resulting in high morbidity and mortality. HPV-DNA testing couple with treatment with thermal ablation is a recommended screening and precancer treatment strategy, but not enough is known about how this can be effectively implemented in the context of integrated services. The (Scale Up Cervical Cancer Elimination by Secondary prevention Strategy, (SUCCESS) project is conducting a study to understand this approach, integrated into existing women’s health services in Burkina Faso, Cote d’Ivoire, Guatemala, and the Philippines. Methods A hybrid effectiveness-implementation type III mixed-methods observational study design is used to assess feasibility, acceptability, and costs of integrated service delivery in 10 sites per country, selected considering urban/rural location, facility level, onsite/offsite laboratories, and health services type. A combined sample size of about 8,000 women aged 25–49 years was computed, 15% being HIV positive, thus about 2000 per country. The primary outcome is proportion of HPV positive women completing precancer treatment, if eligible, within three months of screening. Data collection and analysis includes; facility and client exit surveys, key informant and client interviews, registries and project records extractions, and costing data analysis. Analysis includes descriptive statistics, context description, thematic analysis, and document analysis. Quantitative analyses will be stratified by participant’s HIV status. Discussion Recruitment is ongoing; started in April 2022 (Burkina Faso and Côte d’Ivoire) and August 2022 (Guatemala and the Philippines). Follow-up of study participants 12-months post-treatment is expected to be completed for all countries by April 2024. In LMICs, integrating cervical cancer secondary prevention services into other health services will likely require specific rather than incidental recruitment of women for screening. Reconfiguration of laboratory infrastructure and planning for sample management must be made well in advance to meet induced demand for screening. Registration: ClinicalTrials.Gov ID: NCT05133661 (24/11/2021)
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".